Patent Application
20240194208
Embodiments of the present invention refer to an encoder and a decoder. Further embodiments refer to a method for encoding and decoding and to a corresponding computer program. In general, embodiments of the present invention are in the field of integral band-wise parametric coder.
Modern audio and speech coders at low bit-rates usually employ some kind of parametric coding for at least part of its spectral bandwidth. The parametric coding either is separated from a waveform preserving coder (called core coder with a bandwidth extension in this case) or is very simple (e.g. noise filling).
In the known technology, several approaches in the field of parametric coder are already known.
In [1] comfort noise of a magnitude derived from the transmitted noise fill-in level is inserted in subvectors rounded to zero.
In [2] noise level calculation and noise substitution detection in the encoder comprise:
In [2] noise is introduced into spectral lines quantized to zero starting from a “noise filling start line”, where the magnitudes of the introduced noise is dependent on the mean quantization error and the introduced noise is per band scaled with the scale factors.
In [3] a noise filling in frequency domain coder is proposed, where zero-quantized lines are replaced with a random noise shaped depending on a tonality and the location of the non-zero-quantized lines, the level of the inserted noise set based on a global noise level.
In [4] noise-like components are detected on a coder frequency band basis in the encoder. The spectral coefficients in a scalefactor bands containing noise-like components are omitted from the quantization/coding and only a noise substitution flag and the total power of the substituted bands are transmitted. In the decoder random vectors with the desired total power are inserted for the substituted spectral coefficients.
In [5] a bandwidth extension method operating in the time domain that avoids inharmonicity is proposed. Harmonicity of the decoded signal is ensured by calculation of the autocorrelation function of the magnitude spectrum, where the magnitude spectrum is obtained from the decoded time domain signal. By using the autocorrelation, an estimation of F0 is avoided. The analytical signal of the LF part is generated by Hilbert transformation and multiplied with the modulator to produce the bandwidth extension. Envelope shaping and noise addition is done by the SBR.
In [6] the complete core band is copied into the HF region and afterwards shifted so that the highest harmonic of the core matches with the lowest harmonic of the replicated spectrum. Finally the spectral envelope is reconstructed. The frequency shift, also named the modulation frequency, is calculated based on f0 that can be calculated on encoder side using the full spectrum or on decoder side using only the core band. The proposal also takes advantage of the steep bandpass filters of the MDCT to separate the LF and HF bands.
In [7-14] a semi-parametric coding technique, named the Intelligent Gap Filling (IGF), is proposed that fills spectral holes in the high-frequency region using synthetic HF generated out of low-frequency content and post-processing by parametric side information consisting of the HF spectral and temporal envelope. The IGF range is determined by a user-defined IGF start and a stop frequency. Waveforms, which are deemed necessary to be coded in a waveform preserving way by the core coder, e.g. prominent tones, may also be located above the IGF start frequency. The encoder codes the spectral envelope in IGF-range and afterwards quantizes the MDCT spectrum. The decoder uses traditional noise filling below the IGF start frequency. A tabulated user-defined partitioning of the spectrum bandwidth is used with a possible signal adaptive choice of the source partition (tile) and with a post-processing of the tiles (e.g. cross-fading) for reducing problems related to tones at tile borders.
In [11] an automated selection of source-target tile mapping and whitening level in IGF is proposed, based on a psychoacoustic model.
In [15] the encoder finds extremum coefficients in a spectrum, modifies the extremum coefficient or its neighboring coefficients and generates side information, so that pseudo coefficients are indicated by the modified spectrum and the side information. Pseudo coefficients are determined in the decoded spectrum and set to a predefined value in the spectrum to obtain a modified spectrum. A time-domain signal is generated by an oscillator controlled by the spectral location and value of the pseudo coefficients. The generated time-domain signal is mixed with the time-domain signal obtained from the modified spectrum.
In [16] pseudo coefficients are determined in the decoded spectrum and replaced by a stationary tone pattern or a frequency sweep pattern.
In [17][18] quantizers use a dead-zone that is adapted depending on the input signal characteristics. The dead-zone makes sure that low-level spectral coefficients, potentially noisy coefficients, are quantized to zero.
Below, drawbacks of the known technology will be discussed, wherein the analysis of the known technology and the identification of the drawbacks is part of the invention.
In the known technology either just simple noise filling is integrated in the core coder [1][2][3][4], the core coder being the waveform preserving quantizer for spectral lines, or there is a distinction between the core coder and the bandwidth extension [1][5][6][7-14]. Even though the IGF [7-14] allows preservation of spectral lines in the whole bandwidth, it requires a spectral analyzer operating before the spectral domain encoder and thus it is not possible to have a choice, which parts of the spectrum to code parametrically depending on the result of the spectral domain encoder. The PNS in [4] decides before the quantization, just depending on tonality, which sub-bands to zero out and uses only random noise for the sub-bands substitution.
In [15] only parametric coding of single tonal components is considered. It is decided before the quantizer, which spectral lines to code parametrically and only simple maxima determination is used for the decision. The result of the quantizer is not used for determining which spectral lines to code parametrically. Non-zero pseudo coefficients need to be coded in the spectrum and coding non-zero coefficients is in almost all cases more expensive than coding zero coefficients. On top of coding the pseudo coefficients, a side information is required to distinguish pseudo coefficients from the waveform preserving spectral coefficients. Thus, a lot of information needs to be transmitted in order to generate a signal with many tonal components. The method also does not propose any solution for non-tonal parts of a signal. In addition, the computational complexity for generating signals containing many tonal components coded parametrically is very high.
In [16] the high computation complexity is reduced compared to [15], by using spectral patterns instead of time-domain generator. Yet only predetermined patterns or their modifications are used for replacing the pseudo coefficients, thus either requiring a lot of storage or limiting the range of the possible tones that can be generated. The other drawbacks from [15] remain in [16].
The noise filling in [1][2][3] and similar methods provide substitution of spectral lines quantized to zero, but with very low spectral resolution, usually just using a single level for the whole bandwidth.
The IGF has predefined sub-band partitioning and the spectral envelope is transmitted for the complete IGF range, without a possibility to adaptively transmit the spectral envelope only for some sub-bands.
In [5] only the characteristics of the autocorrelation of the magnitude spectrum and predefined constants are used for choosing the offset used in the modulator. Only one offset is found for the whole spectrum bandwidth.
In [6] only one modulation frequency for the whole bandwidth is used for the frequency shift and the modulation frequency is calculated only on the basis of the fundamental frequency. In [11] only predefined source tiles below the IGF start frequency are used to fill the IGF target range, where the target range is above the start frequency. The tile choice is dictated by the adaptive encoding and thus needs to be coded in the bit-stream. The proposed brute force approach has high computational complexity.
In IGF a source tile is obtained bellow the IGF start frequency and thus does not use the waveform preserving core coded prominent tones located above the IGF start frequency. There is also no mention of using combined low-frequency content and the waveform-preserving core coded prominent tones located above the IGF start frequency as a source tile. This shows that the IGF is a tool that is an addition to a core coder and not an integral part of a core coder.
The methods that use dead-zone [17][18] try to estimate value range of spectral coefficients that should be set to zero. As they are not using the actual output of the quantization, they are prone to errors in the estimation.
It is an objective of the of the present invention to provide a concept for efficient coding, especially efficient parametric coding.
According to an embodiment, an encoder for encoding a spectral representation of audio signal divided into a plurality of sub-bands, wherein the spectral representation consists of frequency bins or of frequency coefficients and wherein at least one sub-band contains more than one frequency bin, may have: a quantizer configured to generate a quantized representation of the spectral representation of audio signal divided into the plurality sub-bands; and a band-wise parametric coder configured to provide a coded parametric representation of the spectral representation depending on the quantized representation, wherein the coded parametric representation consists of parameters describing the spectral representation in the sub-bands or coded versions of the parameters describing the spectral representation in the sub-bands; wherein there are at least two sub-bands being different and the parameters describing the spectral representation in the at least two sub-bands being different wherein the parameters describe the energy in the sub-bands; wherein at least one sub-band of the plurality of sub-bands is quantized to zero or wherein a spectral representation for at least one sub-band of the plurality of sub-bands is zero in the quantized representation.
According to another embodiment, a decoder for decoding an encoded audio signal, the encoded audio signal consisting of at least a coded representation of spectrum and a coded parametric representation, wherein the encoded audio signal further has a quantization step, may have: a spectral domain decoder configured for generating a decoded and dequantized spectrum from the coded representation of spectrum and quantization step, wherein the decoded and dequantized spectrum is divided into sub-bands; band-wise parametric decoder is configured to identify zero sub-bands in a decoded spectrum or the decoded and dequantized spectrum and to decode a parametric representation of the zero sub-bands based on the coded parametric representation, wherein the parametric representation hasa parameters describing the energy in the zero sub-bands and wherein there are at least two sub-bands being different and, thus, parameters in at least two sub-bands being different and wherein the coded parametric representation is represented by use of a variable number of bits and wherein the number of bits used for representing the coded parametric representation is dependent on the coded representation of spectrum.
According to another embodiment, a decoder for decoding an encoded audio signal may have: a spectral domain decoder configured for generating a decoded and dequantized spectrum dependent on the encoded audio signal, wherein the decoded and dequantized spectrum is divided into sub-bands; a band-wise parametric decoder configured to identify zero sub-bands in a decoded spectrum or a decoded and dequantized spectrum and to decode a parametric representation of the zero sub-bands based on the encoded audio signal; aa band-wise spectrum generator configured to generate a band-wise generated spectrum dependent on the parametric representation of the zero sub-bands; a combiner configured to provide a band-wise combined spectrum; where the band-wise combined spectrum has a combination of the band-wise generated spectrum and the decoded and dequantized spectrum or a combination of the band-wise generated spectrum and a combination of a predicted spectrum and the decoded and dequantized spectrum and a spectrum-time converter configured for converting the band-wise combined spectrum or a derivative of the band-wise combined spectrum into a time representation.
Another embodiment may have a band-wise parametric spectrum generator configured to generate a spectrum to obtain a generated spectrum that is added to a decoded and dequantized spectrum or to a combination of a predicted spectrum and the decoded and dequantized spectrum, where the generated spectrum is band-wise obtained from a source spectrum, the source spectrum being one of:
According to another embodiment, a method for encoding a spectral representation of audio signal divided into a plurality of sub-bands, wherein the spectral representation consists of frequency bins or of frequency coefficients and wherein at least one sub-band contains more than one frequency bin, may have the steps of: generating a quantized representation of the spectral representation of audio signal divided into plurality sub-bands; providing a coded parametric representation of the spectral representation depending on the quantized representation, wherein the coded parametric representation consists of parameters describing the spectral representation in the sub-bands or coded versions of the parameters describing the spectral representation in the sub-bands; wherein there are at least two sub-bands being different and the parameters describing the spectral representation in the at least two sub-bands being different wherein the parameters describe the energy in the sub-bands; wherein at least one sub-band of the plurality of sub-bands is quantized to zero or wherein a spectral representation for at least one sub-band of the plurality of sub-bands is zero in the quantized representation.
According to another embodiment, a method for decoding an encoded audio signal, the encoded audio signal consisting of at least a coded representation of spectrum and a coded parametric representation, wherein the encoded audio signal further has a quantization step, may have the steps of: generating a decoded and dequantized spectrum from the coded representation of spectrum and quantization step, wherein the decoded and dequantized spectrum is divided into sub-bands; identifying zero sub-bands in a decoded spectrum or the decoded and dequantized spectrum and decoding a parametric representation of the zero sub-bands based on the coded parametric representation, wherein the parametric representation has parameters describing the energy in the zero sub-bands and wherein there are at least two sub-bands being different and, thus, parameters in at least two sub-bands being different and wherein the coded parametric representation is represented by use of a variable number of bits and wherein the number of bits used for representing the coded parametric representation is dependent on the coded representation of spectrum.
According to another embodiment, a method for decoding an encoded audio signal may have the steps of: generating a decoded and dequantized spectrum based on an encoded audio signal, wherein the decoded and dequantized spectrum is divided into sub-bands; identifying zero sub-bands in a decoded spectrum or the decoded and dequantized spectrum and to decode a parametric representation of the zero sub-bands based on the encoded audio signal; generating a band-wise generated spectrum dependent on the parametric representation of the zero sub-band; providing a band-wise combined spectrum; where the band-wise combined spectrum has a combination of the band-wise generated spectrum and the decoded and dequantized spectrum or a combination of the band-wise generated spectrum and a combination of a predicted spectrum and the decoded and dequantized spectrum; and converting the band-wise combined spectrum or a derivative of the band-wise combined spectrum into a time representation.
Another embodiment may have a method for generating a band-wise generated spectrum, having the step of generating a spectrum to obtain a generated spectrumthat is added to a decoded and dequantized spectrum or to a combination of a predicted spectrum and the decoded and dequantized spectrum, where the generated spectrum is band-wise obtained from a source spectrum, the source spectrum being one of:
Another embodiment may have a non-transitory digital storage medium having stored thereon a computer program for performing the above inventive methods when the computer medium is run by a computer.
An embodiment provides an encoder for encoding a spectral representation of audio signal (XMR) divided into a plurality of sub-bands, wherein the spectral representation (XMR) consists of frequency bins or of frequency coefficients and wherein at least one sub-band contains more than one frequency bin. The encoder comprises a quantizer and a band-wise parametric coder. The quantizer is configured to generate a quantized representation (XQ) of the spectral representation of audio signal (XMR) divided into plurality sub-bands. The band-wise parametric coder is configured to provide a coded parametric representation (zfl) of the spectral representation (XMR) depending (based) on the quantized representation (XQ), e.g. in a band-wise manner, wherein the coded parametric representation (zfl) consists of a parameter describing energy in sub-bands or a coded version of parameters describing energy in sub-bands; wherein there are at least two sub-bands being different and, thus, the corresponding parameters describing energy in at least two sub-bands are different. Note the at least two sub-bands may belong to the plurality of sub-bands.
An aspect of the present invention is based on finding that an audio signal or a spectral representation of the audio signal divided into a plurality of sub-bands can be efficiently coded in a band-wise manner (band-wise may mean per band/sub-band). According to embodiments the concept allows restricting the parametric coding only in the sub-bands that are quantized to zero by a quantizer (used for quantizing the spectrum). This concept enables an efficient joint coding of a spectrum and band-wise parameters, so that a high spectral resolution for the parametric coding is achieved, yet lower than the spectral resolution of a spectral coder can be achieved. The resulting coder is defined as an integral band-wise parametric coding entity within a waveform preserving coder. According to embodiments, the band-wise parametric coder together with a spectrum coder are configured to jointly obtain a coded version of the spectral representation of audio signal (XMR). This joint coder concept has the benefit that the bitrate distribution between the two coders may be done jointly.
According to further embodiments, at least one sub-band is quantized to zero. For example, the parametric coder determines which sub-bands are zero and codes (just) a representation for the sub-bands that are zero. According to embodiments, at least two sub-bands may have different parameters.
According to embodiments the spectral representation is perceptually flattened. This may be done, for example, by use of a spectral shaper which is configured for providing a perceptually flattened spectral representation from the spectral representation based on a spectral shape obtained from a coded spectral shape. Note, the perceptually flattened spectral representation is divided into sub-bands of different or higher frequency resolution than the coded spectral shape.
According to further embodiments, the encoder may further comprise a time-spectrum converter, like an MDCT converter configured to convert an audio signal having a sampling rate into a spectral representation. Starting from said enhancements, the band-wise parametric coder is configured to provide parametric representation of the perceptually flattened spectral representation, or a derivative of the spectrally flattened spectral representation, where the parametric representation may depend on the optimal quantization step and may consist of parameters describing energy in sub-bands, wherein the quantized spectrum is zero, so that at least two sub-bands have different parameters or that at least one parameter is restricted to only one sub-band.
According to further embodiments, the spectral representation is used to determine the optimal quantization step. For example, the encoder can be enhanced by use of a so called rate distortion loop configured to determine a quantization step. This enables that said rate distortion loop determines or estimates an optimal quantization step as used above. This may be done in that way, that said loop performs several (at least two) iteration steps, wherein the quantization step is adapted dependent on one or more previous quantization steps.
In order to code the representation of the quantized spectrum the encoder may further comprise a lossless spectrum coder. According to further embodiments the encoder comprises the spectrum coder and/or spectrum coder decision entity configured to provide a decision if a joint coding of the coded representation of the quantized spectrum and a coded representation of the parametric representation fulfills a constraint that a total number of bits for the joint coding is below a predetermined threshold. This especially makes sense, when both the encoded representation of the quantized spectrum and the coded representation of the parametric spectrum are based on a variable number of bits (optional feature) dependent on the spectral representation or dependent on a derivative of the perceptually flattened spectral representation and the quantization step. According to further embodiments both the band-wise parametric coder as well as the spectrum coder form a joint coder which enables the interaction, e.g., to take into account parameters used for both, e.g. the variable number of bits or the quantization step.
According to further embodiments the encoder further comprises a modifier configured to adaptively set at least a sub-band in the quantization step to zero dependent on a content of the sub-band in the quantized spectrum and/or in the spectral representation.
According to further embodiments the band-wise parametric coder comprises two stages, wherein the first stage of the two stages of the band-wise parametric coder is configured to provide individual parametric representations of the sub-bands above a frequency, and where the second stage of the two stages provides an additional average parametric representation for the sub-bands above the frequency, e.g. based on the parametric representations of the (individual) sub-bands, where the individual parameter representation is zero and for sub-bands below the frequency.
According to an embodiment this encoder may be implemented by a method, namely a method for encoding an audio signal comprising the following steps:
Here, there are at least two sub-bands that are different and, thus, the parameters describing energy in at least two sub-bands are different.
Another embodiment provides a decoder. The decoder comprises a spectral domain decoder and band-wise parametric decoder. The spectral domain decoder is configured for generating a decoded spectrum or dequantized (and decoded) spectrum based on an encoded audio signal, wherein the decoded spectrum is divided into sub-bands. Optionally the spectral domain decoder uses for the decoding/dequantizing an information on a quantization step. The band-wise parametric decoder is configured to identify zero sub-bands in the decoded and/or dequantized spectrum and to decode a parametric representation of the zero sub-bands based on the encoded audio signal. Here, wherein the parametric representation comprises parameters describing the sub-bands, e.g. energy in the sub-bands, and wherein there are at least two sub-bands being different and, thus, parameters describing the at least two sub-bands being different; note the identifying can be performed based on the decoded and dequantized spectrum or just a spectrum, referred to as decoded spectrum, processed by the spectral domain decoder without the dequantization step. additionally or alternatively, the coded parametric representation is coded by use of a variable number of bits and/or wherein the number of bits used for representing the coded parametric representation is dependent on the spectral representation of audio signal. Expressed in other words, this means, that the decoder is configured to generate a decoded output from a jointly coded spectrum and band-wise parameters.
Another embodiment provides another decoder, having the following entities: spectral domain decoder, band-wise parametric decoder in combination with band-wise spectrum generator, a combiner, and spectrum-time converter. The spectral domain decoder, band-wise parametric decoder may be defined described as above; alternatively another parametric decoder, like from the IGF (cf. [7-14]) may be used. The band-wise spectrum generator is configured to generate a band-wise generated spectrum dependent on the parametric representation of the zero sub-bands. The combiner is configured to provide a band-wise combined spectrum, where the band-wise combined spectrum comprises a combination of the band-wise generated spectrum and the decoded spectrum or a combination of the band-wise generated spectrum and a combination of a predicted spectrum and the decoded spectrum. The spectrum-time converter is configured for converting the band-wise combined spectrum or a derivative thereof (e.g. e reshaped spectrum, reshaped by an SNS or TNS or alternatively reshaped by use of a LP predictor) into a time representation.
The band-wise parametric decoder may according to embodiments be configured to decode a parametric representation of the zero sub-bands (EB) based on the encoded audio signal using the quantization step. According to further embodiments the decoder comprises a spectrum shaper which is configured for providing a reshaped spectrum from the band-wise combined spectrum, or a derivative of the band-wise combined spectrum. For example, the spectrum shaper may use spectral shape obtained from a coded spectral shape of different or lower frequency resolution than the sub-band division.
According to further embodiments the parametric representation consists of parameters describing energy in the zero sub-bands, so that at least two sub-bands have different parameters or that at least one parameter is restricted to only one sub-band. Note, the zero sub-bands are defined by the decoded and/or dequantized spectrum output of the spectrum decoder.
According to another embodiment, a band-wise parametric spectrum generator may be provided together with the above decoder or independent. The parametric spectrum generator is configured to generate a generated spectrum that is added to the decoded and dequantized spectrum or to a combination of a predicted spectrum and the decoded spectrum. Note the step of adding to the decoded and dequantized spectrum is, for example performed, when there is no LTP in a system is present. Here, the generated spectrum (XG) may be band-wise obtained from a source spectrum, the source spectrum being one of:
The decoder may be implemented by a method. The method for decoding an audio signal comprises:
Note the parametric representation (EB) comprises parameters describing sub-bands and wherein there are at least two sub-bands being different and, thus, parameters describing at least two sub-bands being different and/or wherein the coded parametric representation (zfl) is coded by use of a variable number of bits and/or wherein the number of bits used for representing the coded parametric representation (zfl) is dependent on the coded representation of spectrum (spect).
Alternatively, the method comprises the following steps:
The above discussed generator may be implemented by a method for generating a generated spectrum that is added to the decoded and dequantized spectrum or to a combination of a predicted spectrum and the decoded spectrum, where the generated spectrum is band-wise obtained from a source spectrum, the source spectrum being one of:
Note the source spectrum can be derived from any of the listed possibilities.
According to embodiments the source spectrum is weighted based on energy parameters of zero sub-bands. According to further embodiments a choice of the source spectrum for a sub-band is dependent on the sub-band position, tonality information, the power spectrum estimation, energy parameters, pitch information and/or temporal information. Note the tonality information may be ϕH, and/or pitch information may be
According to embodiments, the source spectrum is weighted based on the energy parameters of zero bands.
It should be noted, that all of the above-discussed methods may be implemented using a computer program.
Embodiments of the present invention will subsequently be discussed referring to the enclosed figures, wherein:
Below, embodiments of the present invention will subsequently be discussed referring to the enclosed figures, wherein identical reference numerals are provided to objects having identical or similar functions, so that the description thereof is mutually applicable and interchangeable.
According to embodiments, the parametric coder 1010 is coupled with the spectrum coder or lossless spectrum coder 1020, so as to form a joint coder 1010 plus 1020. The signal to be processed by the joint coder 1010 plus 1020 is provided by the quantizer 1030, while the quantizer 1030 uses spectral representation of audio signal XMR divided into plurality sub-bands as input.
The quantizer 1030 quantizes XMR to generate a quantized representation XQ of the spectral representation of audio signal XMR (divided into plurality sub-bands). Optionally, the quantizer may be configured for providing a quantized spectrum of a perceptually flattened spectral representation, or a derivative of the perceptional flattened spectral representation. The quantization may be dependent on the optimal quantization step, which is according to further embodiments determined iteratively (cf.
Both coders 1010 and 1020 receive the quantized representation XQ, i.e. the signal XMR preprocessed by a quantizer 1030 and an optional modifier (not shown in
According to embodiments, the coded parametric representation (zfl) uses variable number of bits. For example the number of bits used for representing the coded parametric representation (zfl) is dependent on the spectral representation of audio signal (XMR).
According to embodiments, the coded representation (spect) uses variable number of bits or that the number of bits used for representing the coded representation (spect) is dependent on the spectral representation of audio signal (XMR). Note the coded representation (spect) may be obtained by the lossless spectrum coder.
According to embodiments, the (sum of) number of bits needed for representing the coded parametric representation (zfl) and the coded representation (spect) may be below a predetermined limit.
According to embodiments, the parameters describe energy only in sub-bands for which the quantized representation (XQ) is zero (that is all frequency bins of XQ in the sub-bands are zero). Other parametric representations of zero sub-bands may be used. This may be a specification of “depending on the quantized representation (XQ)”.
According to embodiments, the band-wise parametric coder 1010 is configured to provide a parametric description of sub-bands quantized to zero. The parametric representation may depend on an optimal quantization step (cf. step size in
The above approach further allows restricting the parametric coding only in the sub-bands that are quantized to zero by a quantizer used for quantizing the spectrum. Due to the usage of a modifier it is additionally possible to provide an adaptive way of distributing bits between the band-wise parametric coder 1010 and the spectrum coder 1020, each of the coder taking into account the bit demand of the other, and allows fulfillment of bitrate limit.
According to further embodiments the encoder 1000 may comprise an entity like a divider (not shown) which is configured to divide the spectral representation of the audio signal into said sub-bands. Optionally or additionally, the encoder 1000 may comprise in the upstream path a TDtoFD transformer (not shown), like the MDCT transformer (cf. entity 152, MDCT or comparable) configured to provide the spectral representation based on a time domain audio signal. Further optional elements are a temporal noise shaping (TNSE cf. 154 of
At the output of the audio signal 1010 plus 1020 a bit stream multiplexer (not shown) may be arranged. The multiplexer has the purpose to combine the band-wise parametric coded and spectrum coded bit stream.
According to embodiments, the output of the MDCT 152 is XM of length LM. For an example at the input sampling rate of 48 kHz and for the example frame length of 20 milliseconds, LM is equal to 960. The codec may operate at other sampling rates and/or at other frame lengths. All other spectra derived from XM: XMS, XMT, XMR, XQ, XD, XDT, XCT, XCS, XC, XP, XPS, XN, XNP, XS may also be of the same length LM, though in some cases only a part of the spectrum may be needed and used. A spectrum consists of spectral coefficients, also known as spectral bins or frequency bins. In the case of an MDCT spectrum, the spectral coefficients may have positive and negative values. We can say that each spectral coefficient covers a bandwidth. In the case of 48 KHz sampling rate and the 20 milliseconds frame length, a spectral coefficient covers the bandwidth of 25 Hz. The spectral coefficients may be for an example indexed from 0 to LM−1.
SNSESNSDNSB=64NSB−1NSB=64 The SNS scale factors, used in and (cf.
In iBPC, “zfl decode” and/or “Zero Filling” blocks, the spectra may be divided into sub-bands Bi of varying length LB
In other example iBPC may be used in a codec where SNSE is replaced with an LP analysis filter at the input of a time to frequency converter (e.g. at the input of 152) and where SNSD is replaced with an LP synthesis filter at the output of a frequency to time converter (e.g. at the output of 161).
According to further embodiments the band-wise parametric coder 1010 is integrated into a rate distortion loop (cf.
Note, although in
With respect to
The spectral domain decoder 1230 (which may comprise a dequantizer in combination with a decoder) is configured for generating a dequantized spectrum (XD) dependent on a quantization step, wherein the dequantized spectrum is divided into sub-bands. The band-wise parametric decoder 1210 identifies zero sub bands i.e., sub-bands consisting only of zeros, in the dequantized spectrum and decodes energy parameters of the zero sub-bands wherein the zero sub-bands are defined by the dequantized spectrum output of the spectrum decoder. For this an information, e.g. regarding the quantized representation (XQ) taken from an output of the spectrum decoder 1230 may be used, since which sub-bands have a parametric representation depends on a decoded spectrum obtained from spect. Note the output of 1230 used as input for 1220 can have an information on the decoded spectrum or an derivative thereof like an information on the dequantized spectrum, since both the decoded spectrum and the dequantized spectrum may have the same zero sub-bands. The decoded spectrum obtained from spect may contain the same information as the input to 1010+1020 in
Starting from this, the band-wise generator 1220 provides a band-wise generated spectrum XG depending on the parametric representation of the zero sub-bands. The combiner 1240 provides a band-wise combined spectrum XCT. For example, for the combined spectrum XCT the following combinations are possible:
In other words the interaction of the entities 1220, 1230 with the entity 1240 can be described as follows: The band-wise parametric spectrum generator 1220 provides a generated spectrum XG that is added to the decoded spectrum or to a combination of the predicted spectrum and the decoded spectrum by the entity 1240. The generated spectrum XG is band-wise obtained from a source spectrum, the source spectrum being a second prediction spectrum XNP or a random noise spectrum XN or the already generated parts of the generated spectrum or a combination of them. Note, XCT may contain XG. The already generated parts of XCT may be used to generate XG. The source spectrum may be weighted based on the energy parameters of zero sub-bands. The choice of the source spectrum for a sub-band may be on the band position, tonality, power spectrum estimation, energy parameters, pitch parameter and temporal information. This method obtains the choice of sub-bands that are parametrically coded based on a decoded spectrum, thus avoiding additional side information in a bit stream. According to another way in this adaptive method, it is decided for each sub-band which source spectrum to use for replacing zeros in a sub-band is provided in the decoder 1200, thus avoiding additional side information in a bit stream and allowing a big number of possibilities for the source spectrum choice.
The output of the combiner 1240 can be further processed by an optional TNS or SNSD (not shown) to obtain a so-called reshaped spectrum. Based on the output of the combiner 1240 or based on this reshaped spectrum the optional spectrum-time converter 1250 outputs a time representation. According to further embodiments, the decoder 1200 may comprise a spectrum shaper for providing a reshaped spectrum from the band-wise combined spectrum of from a derivative of the band-wise combined spectrum.
According to further embodiments the encoder may comprise a spectrum coder decision entity for providing a decision, if a joint coding or a coded representation of the quantized spectrum and a coded representation of the parametric zero sub-bands representation fulfills a constraint that the total number of bits of the joint coding is below a predetermined limit. Here, both the encoded representation of the quantized spectrum and the coded representation of parametric zero sub-bands may use a variable number of the bits dependent on the perceptually flattened spectral representation, or a derivative of the perceptually flattened spectral representation, and/or the quantization step.
As discussed above, the band-wise parametric spectrum generator and combiner 1240 may be implemented as follows. The band-wise parametric spectrum generator provides a generated spectrum in a band-wise manner and adds it to a decoded spectrum or to a combination of a predicted spectrum and the decoded spectrum. The generated spectrum is band-wise obtained from a source spectrum, the source spectrum being a second prediction spectrum or a random noise spectrum of already generated parts of the generated spectrum or a combination of them. The source spectrum may be weighted based on the energy parameters of zero-bands. The use of the already generated parts of the generated spectrum provides a combination of any two distinct parts of the decoded spectrum and thus a harmonic or tonal source spectrum not available by using just one part of the decoded spectrum. The combination of the second prediction spectrum and the source spectrum is another advantage for creating harmonic or tonal source spectrum not available by just using the decoded spectrum.
The main entities of the encoder 101 are marked by the reference numerals 110, 130, 150. The entity 110 performs the pulse extraction, wherein the pulses p are encoded using the entity 132 for pulse coding.
The signal encoder 150 is implemented by a plurality of entities 152, 153, 154, 155, 156, 157, 158, 159, 160 and 161. These entities 152-161 form the main path of the encoder 150, wherein in parallel, additional entities 162, 163, 164, 165 and 166 may be arranged. The entity 162 (zfl decoder) connects informatively the entities 156 (iBPC) with the entity 158 for Zero filling. The entity 165 (get TNS) connects informatively the entity 153 (SNSE) with the entity 154, 158 and 159. The entity 166 (get SNS) connects informatively the entity 152 with the entities 153, 163 and 160. The entity 158 performs zero filling an can comprise a combiner 158c which will be discussed in context of
The entities 163 and 164 receive the pitch contour from the entity 180 and the coded residual yc so as to generate the predicted spectrum XP and/or the perceptually flattened prediction XPS. The functionality and the interaction of the different entities will be described below.
Before discussing the functionality of the encoder 101 and especially of the encoder 150 a short description of the decoder 210 is given. The decoder 210 may comprise the entities 157, 162, 163, 164, 158, 159, 160, 161 as well as encoder specific entities 214 (HPF), 23 (signal combiner) and 22 (for decoding and reconstructing the pulse portion consisting of reconstructed pulse waveforms).
Below, the encoding functionality will be discussed: The pulse extraction 110 obtains an STFT of the input audio signal PCMI, and uses a non-linear magnitude spectrogram and a phase spectrogram of the STFT to find and extract pulses, each pulse having a waveform with high-pass characteristics. Pulse residual signal ym is obtained by removing pulses from the input audio signal. The pulses are coded by the Pulse coding 132 and the coded pulses CP are transmitted to the decoder 201.
yMXMLMXMSNSEXMSTNSEXMTϕHXMXMSXMTLTPXPXPSXMTLTPXMR The pulse residual signal is windowed and transformed via the MDCT 152 to produce of length. The windows are chosen among 3 windows as in [19]. The longest window is 30 milliseconds long with 10 milliseconds overlap in the example below, but any other window and overlap length may be used. The spectral envelope of is perceptually flattened via 153 obtaining. Optionally Temporal Noise Shaping 154 is applied to flatten the temporal envelope, in at least a part of the spectrum, producing. At least one tonality flag in a part of a spectrum (in or or) may be estimated and transmitted to the decoder 201/210. Optionally Long Term Prediction 164 that follows the pitch contour 180 is used for constructing a predicted spectrum from a past decoded samples and the perceptually flattened prediction is subtracted in the MDCT domain from , producing an residual. A pitch contour 180 is obtained for frames with high average harmonicity and transmitted to the decoder 201/210. The pitch contour 180 and a harmonicity is used to steer many parts of the codec. The average harmonicity may be calculated for each frame.
According to embodiments, the encoder splits the input signal into frames and outputs for example for each frame at least one or more of the following parameters:
XPS is coming from the LTP which is also used in the encoder, but the LTP is shown only in the decoder (cf.
The encoding of the XMR (residual from the LTP) output by the entity 155 is done in the integral band-wise parameter coder (iBPC) as will be discussed with respect to
At the output of the bit-stream multiplexer 156mu the band-wise parametric decoder 162 is arranged together with the spectrum decoder 156sd. The entity 162 receives the signal zfl, the entity 156sd the signal spect, where both may receive the global gain/step size gQ0. Note the parametric decoder 162 uses the output XD of the spectrum decoder 156sd for decoding zfl. It may alternatively use another signal output from the decoder 156sd. Background there of is that the spectrum decoder 156sd may comprise two parts, namely a spectrum lossless decoder and a dequantizer. For example, the output of the spectrum lossless decoder may be a decoded spectrum obtained from spect and used as input for the parametric decoder 162. The output of the spectrum lossless decoder may contain the same information as the input XQ of 156pc and 156sc. The dequantizer may use the global gain/step size to derive XD from the output of the spectrum lossless decoder. The location of zero sub-bands in the decoded spectrum and/or in the dequantized spectrum XD may be determined independent of the quantization step qQ
XMR is quantized and coded including a quantization and coding of an energy for zero values in (a part of) the quantized spectrum XQ, where XQ is a quantized version of XMR. The quantization and coding of XMR is done in the Integral Band-wise Parametric Coder iBPC 156. As one of the parts of the iBPC, the quantization (quantizer 156q) together with the adaptive band zeroing 156m produces, based on the optimal quantization step size gQo, the quantized spectrum XQ. The iBPC 156 produces coded information consisting of spect 156sc (that represents XQ) and zfl 162 (that may represent the energy for zero values in a part of XQ).
The zero-filling entity 158 arranged at the output of the entity 157 is illustrated by
The spect is decoded to obtain a dequantized spectrum XD (decoded LTP residual, error spectrum) equivalent to the quantized version of XMR. EB are obtained from zfl taking into account the location of zero values in XD. EB may be a smoothed version of the energy for zero values in XQ. EB may have a different resolution than zfl, advantageously higher resolution coming from the smoothing. After obtaining EB (cf. 162), the perceptually flattened prediction XPS is optionally added to the decoded XD, producing XDT. A zero filling XG is obtained and combined with XDT (for example using addition 158c) in “Zero Filling”, where the zero filling XG consists of a band-wise zero filling
that is iteratively obtained from a source spectrum XS consisting of a band-wise source spectrum
(cf. 156sc) weighted based on EB. XCT is a band-wise combination of the zero filling XG and the spectrum XDT (158c). XS is band-wise constructed (158sg, outputting XG) and XCT is band-wise obtained starting from the lowest sub-band. For each sub-band the source spectrum is chosen (cf. 158sc), for example depending on the sub-band position, the tonality flag (toi), a power spectrum estimated from XDT, EB, pitch information (pii) and temporal information (tei). Note power spectrum estimated from XDT may be derived from XDT or XD. Alternatively a choice of the source spectrum may be obtained from the bit-stream. The lowest sub-bands
in XS up to a starting frequency fZFStart may be set to 0, meaning that in the lowest sub-bands XCT may be a copy of XDT. fZFStart may be 0 meaning that the source spectrum different from zeros may be chosen even from the start of the spectrum. The source spectrum for a sub-band i may for example be a random noise or a predicted spectrum or a combination of the already obtained lower part of XCT, the random noise and the predicted spectrum. The source spectrum XS is weighted based on EB to obtain the zero filling XG.
The weighting may, for example, be performed by the entity 158sg and may have higher resolution than the sub-band division; it may be even sample wise determined to obtain a smooth weighting.
is added to the sub-band i of XDT to produce the sub-band i of XCT. After obtaining the complete XCT, its temporal envelope is optionally modified via TNSD 159 (cf.
The entity “get pitch contour” 180 is described below taking reference to
The process in the block “Get pitch contour 180” will be explained now. The input signal is downsampled from the full sampling rate to lower sampling rate, for example to 8 kHz. The pitch contour is determined by pitch_mid and pitch_end from the current frame and by pitch_start that is equal to pitch_end from the previous frame. The frames are exemplarily illustrated by
The pitch search calculates normalized autocorrelation ρH[dF] of its input and a delayed version of the input. The lags dF are between a pitch search start dFstart and a pitch search end dFend. The pitch search start dFstart, the pitch search end dFend, the autocorrelation length lρH and a past pitch candidate dFpast are parameters of the pitch search. The pitch search returns an optimum pitch dFoptim, as a pitch lag with a fractional precision, and a harmonicity level ρHoptim, obtained from the autocorrelation value at the optimum pitch lag. The range of ρHoptim is between 0 and 1, 0 meaning no harmonicity and 1 maximum harmonicity.
The location of the absolute maximum in the normalized autocorrelation is a first candidate dF1 for the optimum pitch lag. If dFpast is near dF1 then a second candidate dF2 for the optimum pitch lag is dFpast, otherwise the location of the local maximum near dFpast is the second candidate dF2. The local maximum is not searched if dFpast is near dF1, because then dF1 would be chosen again for dF2. If the difference of the normalized autocorrelation at dF1 and dF2 is above a pitch candidate threshold τdF, then dFoptim is set to dF1 (ρH[dF1]−ρH[dF2]>τdF⇒dFoptim=dF1), otherwise dFoptim is set to dF2. τdF is adaptively chosen depending on dF1, dF2 and dFpast, for example τdF=0.01 if 0.75·dF1≤dFpast≤1.25·dF1 otherwise τdF=0.02 if dF1≤dF2 and τdF=0.03 if dF1>dF2 (for a small pitch change it is easier to switch to the new maximum location and if the change is big then it is easier to switch to a smaller pitch lag than to a larger pitch lag).
Locations of the areas for the pitch search in relation to the framing and windowing are shown in
If the average harmonicity is below 0.3 or if norm_corr_end is below 0.3 or if norm_corr_mid is below 0.6 then it is signaled in the bit-stream with a single bit that there is no pitch contour in the current frame. If the average harmonicity is above 0.3 the pitch contour is coded using absolute coding for pitch_end and differential coding for pitch_mid. Pitch_mid is coded differentially to (pitch_start+pitch_end)/2 using 3 bits, by using the code for the difference to (pitch_start+pitch_end)/2 among 8 predefined values, that minimizes the autocorrelation in the pitch_mid area. If there is an end of harmonicity in a frame, e.g. norm_corr_end<norm_corr_mid/2, then linear extrapolation from pitch_start and pitch_mid is used for pitch_end, so that pitch_mid may be coded (e.g. norm_corr_mid>0.6 and norm_corr_end<0.3).
If |pitch_mid-pitch_start|≤τHPFconst and |norm_corr_mid-norm_corr_start|≤0.5 and the expected HPF gains in the area of the pitch_start and pitch_mid are close to 1 and don't change much then it is signaled in the bit-stream that the HPF should use constant parameters.
According to embodiments, the pitch contour provides dcontour a pitch lag value dcontour[i] at every sample i in the current window and in at least dFmax past samples. The pitch lags of the pitch contour are obtained by linear interpolation of pitch_mid and pitch_end from the current, previous and second previous frame.
An average pitch lag
A half pitch lag correction is according to further embodiments also possible.
The LTP buffer 164, which is available in both the encoder and the decoder, is used to check if the pitch lag of the input signal is below dFmin. The detection if the pitch lag of the input signal is below dFmin is called “half pitch lag detection” and if it is detected it is said that “half pitch lag is detected”. The coded pitch lag values (pitch_mid, pitch_end) are coded and transmitted in the range from dFmin to dFmax. From these coded parameters the pitch contour is derived as defined above. If half pitch lag is detected, it is expected that the coded pitch lag values will have a value close to an integer multiple nFcorrection of the true pitch lag values (equivalently the input signal pitch is near an integer multiple nFcorrection of the coded pitch). To extended the pitch lag range beyond the codable range, corrected pitch lag values (pitch_mid_corrected, pitch_end_corrected) are used. The corrected pitch lag values (pitch_mid_corrected, pitch_end_corrected) may be equal to the coded pitch lag values (pitch_mid, pitch_end) if the true pitch lag values are in the codable range. Note the corrected pitch lag values may be used to obtain the corrected pitch contour in the same way as the pitch contour is derived from the pitch lag values. In other words, this enables to extend the frequency range of the pitch contour outside of the frequency range for the coded pitch parameters, producing a corrected pitch contour.
The half pitch detection is run only if the pitch is considered constant in the current window and
If half pitch lag is detected then pitch_mid_corrected and pitch_end_corrected take the value returned by the pitch search for nFmultiple=nFcorrection, otherwise pitch_mid_corrected and pitch_end_corrected are set to pitch_mid and pitch_end respectively.
An average corrected pitch lag
Below the pulse extraction may be discussed in context of
At the output two entities 113c and 113p are arranged, which interact together and receive as input the pitch contour from the entity 180. The entity for choosing the pulses 113c outputs the pulses p directly into another entity 114 producing a waveform. This is the waveform of the pulse and can be subtracted using the mixer 114m from the PCM signal so as to generate the residual signal R (residual after extracting the pulses).
Up to 8 pulses per frame are extracted and coded. In another example other number of maximum pulses may be used. NP
Time-frequency analysis via Short-time Fourier Transform (STFT) is used for finding and extracting pulses (cf. entity 112). In another example other time-frequency representations may be used. The signal PCMI may be high-passed (111hp) and windowed using 2 milliseconds long squared sine windows with 75% overlap and transformed via Discrete Fourier Transform (DFT) into the Frequency Domain (FD). Alternatively, the high pass filtering may be done in the FD (in 112s or at the output of 112s). Thus in each frame of 20 milliseconds there are 40 points for each frequency band, each point consisting of a magnitude and a phase. Each frequency band is 500 Hz wide and we are considering only 49 bands for the sampling rate FS=48 kHz, because the remaining 47 bands may be constructed via symmetric extension. Thus there are 49 points in each time instance of the STFT and 40·49 points in the time-frequency plane of a frame. The STFT hop size is HP=0.0005 FS.
In
The shown entity 112 comprises a spectrogram entity 112s outputting the phase and/or the magnitude spectrogram based on the PCMI signal. The phase spectrogram is forwarded to the pulse extractor 112pe, while the magnitude spectrogram is further processed. The magnitude spectrogram may be processed using a background remover 112br, a background estimator 112be for estimating the background signal to be removed. Additionally or alternatively a temporal envelope determiner 112te and a pulse locator 112pl processes the magnitude spectrogram. The entities 112pl and 112te enable to determine that pulse location(s) which are used as input for the pulse extractor 112pe and the background estimator 112be. The pulse locator finder 112pl may use a pitch contour information. Optionally, some entities, for example, the entity 112be and the entity 112te may use algorithmic representation of the magnitude spectrogram obtained by the entity 112lo.
o.
Below the functionality will be discussed. Smoothed temporal envelope is low-pass filtered version of the temporal envelope using short symmetrical FIR filter (for an example 4th order filter at FS=48 kHz).
Normalized autocorrelation of the temporal envelope is calculated:
where eT is the temporal envelope after mean removal. The exact delay for the maximum
is estimated using Lagrange polynomial of 3 points forming the peak in the normalized autocorrelation.
Expected average pulse distance may be estimated from the normalized autocorrelation of the temporal envelope and the average pitch lag in the frame:
where for the frames with low harmonicity, {tilde over (D)}P is set to 13, which corresponds to 6.5 milliseconds.
Positions of the pulses are local peaks in the smoothed temporal envelope with the requirement that the peaks are above their surroundings. The surrounding is defined as the low-pass filtered version of the temporal envelope using simple moving average filter with adaptive length; the length of the filter is set to the half of the expected average pulse distance ({tilde over (D)}P). The exact pulse position ({dot over (t)}P
Up to 8 pulses per 20 milliseconds are found; if more pulses are detected then smaller pulses are disregarded. The number of found pulses is denoted as NP
Magnitudes are enhanced based on the pulse positions so that the enhanced STFT, also called enhanced spectrogram, consists only of the pulses. The background of a pulse is estimated as the linear interpolation of the left and the right background, where the left and the right backgrounds are mean of the 3rd to 5th time instance away from the (temporal) center position. The background is estimated in the log magnitude domain in 112be and removed by subtracting it in the linear magnitude domain in 112br. Magnitudes in the enhanced STFT are in the linear scale. The phase is not modified. All magnitudes in the time instances not belonging to a pulse are set to zero.
The start frequency of a pulse is proportional to the inverse of the average pulse distance (between nearby pulse waveforms) in the frame, but limited between 750 Hz and 7250 Hz:
The start frequency (fP
The change of the starting frequency in consecutive pulses is limited to 500 Hz (one STFT band). Magnitudes of the enhanced STFT bellow the starting frequency are set to zero in 112pe.
Waveform of each pulse is obtained from the enhanced STFT in 112pe. The pulse waveform is non-zero in 4 milliseconds around its (temporal) center and the pulse length is LW
Each pulse Pi is uniquely determined by the center position tP
Features are calculated for each pulse:
The local energy is calculated from the 11 time instances around the pulse center in the original STFT. All energies are calculated only above the start frequency.
The distance between a pulse pair dP
The value of (xP
Error between the pitch and the pulse distance is calculated as:
Introducing multiple of the pulse distance (k·dP
Probability of a pulse with the relation only to the already coded past pulses is defined as:
Probability (cf. entity 113p) of a pulse (pP
p
P
={dot over (p)}
P
+
p
P
=max(P
p
P
=min(pP
At the end of this procedure, there are NP
Below, with respect to
Pulses are coded using parameters:
and if
is not zero:
A single coded pulse is determined by parameters:
and if
is not zero;
From the parameters that determine the single coded pulse a waveform can be constructed that present the single coded pulse. We can then also say that the coded pulse waveform is determined by the parameters of the single coded pulse.
The number of pulses is Huffman coded.
The first pulse position tP
The first pulse starting frequency fP
The spectrally flatten, e.g. performed using STFT (cf. entity 132fs of
All pulses in the frame may use the same spectral envelope (cf. entity 132as) consisting of eight bands. Band border frequencies are: 1 kHz, 1.5 kHz, 2.5 kHz, 3.5 kHz, 4.5 kHz, 6 kHz, 8.5 kHz, 11.5 kHz, 16 kHz. Spectral content above 16 kHz is not explicitly coded. In another example other band borders may be used.
Spectral envelope in each time instance of a pulse is obtained by summing up the magnitudes within the envelope bands, the pulse consisting of 5 time instances. The envelopes are averaged across all pulses in the frame. Points between the pulses in the time-frequency plane are not taken into account.
The values are compressed using fourth root and the envelopes are vector quantized. The vector quantizer has 2 stages and the 2nd stage is split in 2 halves. Different codebooks exist for frames with
The quantized envelope may be smoothed using linear interpolation. The spectrograms of the pulses are flattened using the smoothed envelope (cf. entity 132fs). The flattening is achieved by division of the magnitudes with the envelope (received from the entity 132as), which is equivalent to subtraction in the logarithmic magnitude domain. Phase values are not changed. Alternatively a filter processor may be configured to spectrally flatten magnitudes or the pulse STFT by filtering the pulse waveform in the time domain.
Waveform of the spectrally flattened pulse yP
The entity 132pc of
According to embodiments the most similar previously quantized pulse is found among NPP pulses from the previous frames and already quantized pulses from the current frame. The correlation ρP
relative to the currently coded pulse, is used in the pulse coding. Up to four relative prediction source indexes
are grouped and Huffman coded. The grouping and the Huffman codes are dependent on NP
The offset for the maximum correlation is the pulse prediction offset
It is coded absolutely, differentially or relatively to an estimated value, where the estimation is calculated from the pitch lag at the exact location of the pulse dP
Gain
that maximizes the SNR Is used for scaling the prediction {tilde over (z)}P
is set to zero.
The prediction residual is quantized using up to four impulses. In another example other maximum number of impulses may be used. The quantized residual consisting of impulses is named innovation żP
The first entity 132bp for finding the best prediction uses the past pulse(s) and the pulse waveform to determine the iSOURCE, shift, GP′ and prediction residual. The quantize impulse entity 132gi quantizes the prediction residual and outputs GI′ and the impulses. The entity 132ce is configured to calculate and apply a correction factor. All this information together with the pulse waveform are received by the entity 132ce for correcting the energy, so as to output the coded impulse. The following algorithm may be used according to embodiments:
:
For finding and coding the impulses the following algorithm is used:
|x|P
└x┘P
└x┘P
Notice that the impulses may have the same location. Locations of the pulses are ordered by their distance from the pulse center. The location of the first impulse is absolutely coded. The locations of the following impulses are differentially coded with probabilities dependent on the position of the previous impulse. Huffman coding is used for the impulse location. Sign of each impulse is also coded. If multiple impulses share the same location then the sign is coded only once.
The resulting 4 found and scaled impulses 15i of the residual signal 15r are illustrated by
may be scaled accordingly, e.g. impulse +/−1 multiplied by Gain
Gain
that maximizes the SNR is used for scaling the innovation żP
The first estimate for quantization of the flattened pulse waveform źP
where Q( ) denotes quantization.
Because the gains are found by maximizing the SNR, the energy of źP
The final gains are then:
The memory for the prediction is updated using the quantized flattened pulse waveform zP
At the end of coding of NP
Below, taking reference to
The entity 220 comprises a plurality of sub-entities, for example, the entity 220cpw for constructing spectrally flattened pulse waveform, an entity 224 for generating a pulse spectrogram (phase and magnitude spectrogram) of the spectrally flattened pulse waveform and an entity 226 for spectrally shaping the pulse magnitude spectrogram. This entity 226 uses a magnitude spectrogram as well as a pulse spectral envelope. The output of the entity 226 is fed to a converter for converting the pulse spectrogram to a waveform which is marked by the reference numeral 228. This entity 228 receives the phase spectrogram as well as the spectrally shaped pulse magnitude spectrogram, so as to reconstruct the pulse waveform. It should be noted, that the entity 220cpw (configured for constructing a spectrally flattened pulse waveform) receives at its input a signal describing a coded pulse. The constructor 220cpw comprises a kind of feedback loop including an update memory 229. This enables that the pulse waveform is constructed taking into account past pulses. Here the previously constructed pulse waveforms are fed back so that past pulses can be used by the entity 220cpw for constructing the next pulse waveform. Below, the functionality of this pulse reconstructor 220 will be discussed. To be noted that at the decoder side there are only the quantized flattened pulse waveforms (also named decoded flattened pulse waveforms or coded flattened pulse waveforms) and since there are no original pulse waveforms on the decoder side, we use the flattened pulse waveforms for naming the quantized flattened pulse waveforms at the decoder side and the pulse waveforms for naming the quantized pulse waveforms (also named decoded pulse waveforms or coded pulse waveforms or decoded pulse waveforms).
For reconstructing the pulses on the decoder side 220, the quantized flattened pulse waveforms are constructed (cf. entity 220cpw) after decoding the gains
impulses/innovation, prediction source
and offset
The memory 229 for the prediction is updated in the same way as in the encoder in the entity 132m. The STFT (cf. entity 224) is then obtained for each pulse waveform. For example, the same 2 milliseconds long squared sine windows with 75% overlap are used as in the pulse extraction. The magnitudes of the STFT are reshaped using the decoded and smoothed spectral envelope and zeroed out below the pulse starting frequency fP
The reconstructed pulse waveforms are concatenated based on the decoded positions tpp inserting zeros between the pulses in the entity 22′ in
The reconstructed pulse waveforms are concatenated based on the decoded positions tP
The reconstructed pulse waveform are not perfect representations of the original pulses. Removing the reconstructed pulse waveform from the input would thus leave some of the transient parts of the signal. As transient signals cannot be well presented with an MDCT codec, noise spread across whole frame would be present and the advantage of separately coding the pulses would be reduced. For this reason the original pulses are removed from the input.
According to embodiments the HF tonality flag ϕH may be defined as follows:
Normalized correlation ρHF is calculate on yMHF between the samples in the current window and a delayed version with
nHFTonalCurrnHFTonal=0.5·nHFTonal+nHFTonalcurr For each MDCT frequency bin above a specified frequency, it is determined, as in 5.3.3.2.5 of [20], if the frequency bin is tonal or noise like. The total number of tonal frequency bins is calculated in the current frame and additionally smoothed total number of tonal frequencies is calculated as.
HF tonality flag ϕH is set to 1 if the TNS is inactive and the pitch contour is present and there is tonality in high frequencies, where the tonality exists in high frequencies if ρHF>0 or nHFTonal>1.
With respect to
In case, the spectrum is not codeable, the process having the steps 311 and 312 together with the verifying step (spectrum now codebale) 313 is applied. After that the step size is increased (cf. 314) before initiating the next iteration (cf. step 308).
A spectrum XMR, which spectral envelope is perceptually flattened, is scalar quantized using single quantization step size gQ across the whole coded bandwidth and entropy coded for example with a context based arithmetic coder producing a coded spect. The coded spectrum bandwidth is divided into sub-bands Bi of increasing width LB
The optimal quantization step size gQo, also called global gain, is iteratively found as explained.
In each iteration the spectrum XMR is quantized in the block Quantize 301 to produce XQ1. In the block “Adaptive band zeroing” 302 a ratio of the energy of the zero quantized lines and the original energy is calculated in the sub-bands Bi and if the energy ratio is above an adaptive threshold τB
where the flags
indicate if a sub-band was zeroed-out in the previous frame:
For each zeroed-out sub-band a flag
is set to one. At the end of processing the current frame,
are copied to
Alternatively there could be more than one tonality flag and a mapping from the plurality of the tonality flags into tonality of each sub-band, producing a tonality value for each sub-band
The values of τB
A frequency range where the adaptive band zeroing is used may be restricted above a certain frequency fABZStart, for example 7000 Hz, extending the adaptive band zeroing as long, as the lowest sub-band is zeroed out, down to a certain frequency fABZMin, for example 700 Hz.
The individual zero filling levels (individual zfl) of sub-bands of XQ1 above fEZ, where fEZ is for an example 3000 Hz that are completely zero is explicitly coded and additionally one zero filling level (zflsmall) for all zero sub-bands bellow fEZ and all zero sub-bands above fEZ quantized to zero is coded. A sub-band of XQ1 may be completely zero because of the quantization in the block Quantize even if not explicitly set to zero by the adaptive band zeroing. The required number of bits for the entropy coding of the zero filling levels (zfl consisting of the individual zfl and the zflsmall) and the spectral lines in XQ1 is calculated (e.g. by the band-wise parametric coder). Additionally the number of spectral lines NQ that can be explicitly coded with the available bit budget is found. NQ is an integral part of the coded spect and is used in the decoder to find out how many bits are used for coding the spectrum lines; other methods for finding the number of bits for coding the spectrum lines may be used, for example using special EOF character. As long as there is not enough bits for coding all non-zero lines, the lines in XQ1 above NQ are set to zero and the required number of bits is recalculated.
For the calculation of the bits needed for coding the spectral lines, bits needed for coding lines starting from the bottom are calculated. This calculation is needed only once as the recalculation of the bits needed for coding the spectral lines is made efficient by storing the number of bits needed for coding n lines for each n≤NQ.
In each iteration, if the required number of bits exceeds the available bits, the global gain gQ is decreased (307), otherwise gQ is increased (314). In each iteration the speed of the global gain change is adapted. The same adaptation of the change speed as in the rate-distortion loop from the EVS may be used to iteratively modify the global gain. At the end of the iteration process, the optimal quantization step size gQo is equal to gQ that produces optimal coding of the spectrum, for example using the criteria from the EVS, and XQ is equal to the corresponding XQ1.
Instead of an actual coding, an estimation of maximum number of bits needed for the coding may be used. The output of the iterative process is the optimal quantization step size gQo; the output may also contain the coded spect and the coded noise filling levels (zfl), as they are usually already available, to avoid repetitive processing in obtaining them again.
Below, the zero-filling will be discussed in detail.
According to embodiments, the block “Zero Filling” will be explained now, starting with an example of a way to choose the source spectrum.
For creating the zero filling, following parameters are adaptively found:
The optimal copy-up distance {dot over (d)}C determines the optimal distance if the source spectrum is the already obtained lower part of XCT. The value of {dot over (d)}C is between the minimum {dot over (d)}Č, that is for an example set to an index corresponding to 5600 Hz, and the maximum {dot over (d)}Ĉ, that is for an example set to an index corresponding to 6225 Hz. Other values may be used with a constraint {dot over (d)}Č<{dot over (d)}Ĉ.
The distance between harmonics
is calculated from an average pitch lag
may be obtained by analyzing XDT or a derivative of it (e.g. from a time domain signal obtained using XDT). The distance between harmonics
is no necessarily an integer. If
is set to zero, where zero is a way of signaling that there is no meaningful pitch lag.
The value of
is the minimum multiple of the harmonic distance
larger than the minimal optimal copy-up distance {dot over (d)}Č:
If
is zero then
is not used.
The starting TNS spectrum line plus the TNS order is denoted as iT, it can be for example an index corresponding to 1000 Hz.
If TNS is inactive in the frame iC
If TNS is active iC
if HFs are tonal (e.g. if ϕH is one).
Magnitude spectrum ZC is estimated from the decoded spect XDT:
A normalized correlation of the estimated magnitude spectrum is calculated:
The length of the correlation LC is set to the maximum value allowed by the available spectrum, optionally limited to some value (for example to the length equivalent of 5000 Hz).
Basically we are searching for n that maximizes the correlation between the copy-up source ZC[iC
We choose dC
and for every m≤dC
If the TNS is active we may choose {dot over (d)}C=dC
If the TNS is inactive {dot over (d)}C=C (ρC,dC
{grave over (d)}C,{grave over (ρ)}C[{grave over (d)}C],
{grave over (ϕ)}TC returns either dC
or {grave over (d)}C. The decision which value to return in C i primarily based on the values
and ρC[{grave over (d)}C]. If the flag {grave over (ϕ)}T
are valid then ρC[{grave over (d)}C] is ignored. The values of {grave over (ρ)}C[{grave over (d)}C] and
are used in rare cases.
In an example C could be defined with the following decisions:
for at least
and larger than ρC[{grave over (d)}C] for at least τ{grave over (d)}
and τ{grave over (d)}
and |dC
is returned if
is larger than ρC[{grave over (d)}C] for at least a threshold, for example 0.2
is returned if {grave over (ϕ)}T
is valid, that is if there is a meaningful pitch lag
is returned if {grave over (ρ)}C[{grave over (d)}C] is small, for example below 0.1, and the value of
is valid, wat is ir there is a meaningful pitch lag, and the pitch lag change from the previous frame is small
The flag {grave over (ϕ)}T
The percentual chang of
is also calculated.
The copy-up distance shift ΔC is set to
unless the optimal copy-up distance {dot over (d)}C is equivalent to {grave over (d)}C and
(τΔ
is a measure of change (e.g. a percentual change) of
is the perceptual change of
The minimum copy up source start ŠC can for an example be set to iT if the TNS is active, optionally lower bound by
if HFs are tonal, or for an example set to [2.54ΔC] if the TNS is not active in the current frame.
The minimum copy-up distance ďC is for an example set to ┌ΔC┐ if the TNS is inactive. If TNS is active, ďC is for an example set to šC if HF are not tonal, or ďC is set for an example to
if HFs are tonal.
Using for example XN[−1]=Σn 2n|XD[n]| as an initial condition, a random noise spectrum XN is constructed as XN[n]=short(31821XN[n−1]+13849), where the function short truncates the result to 16 bits. Any other random noise generator and initial condition may be used. The random noise spectrum XN is then set to zero at the location of non-zero values in XD and optionally the portions in XN between the locations set to zero are windowed, in order to reduce the random noise near the locations of non-zero values in XD.
For each sub-band Bi of length LB
is found. The sub-band division may be the same as the sub-band division used for coding the zfl, but also can be different, higher or lower.
For an example if TNS is not active and HFs are not tonal then the random noise spectrum XN is used as the source spectrum for all sub-bands. In another example XN is used as the source spectrum for the sub-bands where other sources are empty or for some sub-bands which start below minimal copy-up destination: šC+min(ďC,LB
In another example if the TNS is not active and HFs are tonal, a predicted spectrum XNP may be used as the source for the sub-bands which start below šC+{dot over (d)}C and in which EB is at least 12 dB above EB in neighboring sub-bands, where the predicted spectrum is obtained from the past decoded spectrum or from a signal obtained from the past decoded spectrum (for example from the decoded TD signal).
For cases not contained in the above examples, distance dC may be found so that XCT[sC+m](0≤m<LB
that starts at jB
and dc may be set to
for example to the smallest such integer n. If the TNS is not active, another positive integer n may be found so that jB
In another example the lowest sub-bands
in XS up to a starting frequency fZFStart may be set to 0, meaning that in the lowest sub-bands XCT may be a copy of XDT.
An example of weighting the source spectrum based on EB in the block “Zero Filling” is given now.
In an example of smoothing the EB, EB
The scaling factor αC
Additionally the scaling is limited with the factor bC
The source spectrum band
[m] (0≤m<LB
Note in the above explanation, αC
is derived using
and gC
Even with this further embodiment, in which EB may be derived using gQ
The scaled source spectrum band
where the scaled source spectrum band is
is added to XDT[jB
An example of quantizing the energies of the zero quantized lines (as a part of iBPC) is given now.
XQZ is obtained from XMR by setting non-zero quantized lines to zero. For an example the same way as in XN, the values at the location of the non-zero quantized lines in XQ are set to zero and the zero portions between the non-zero quantized lines are windowed in XMR, producing XQZ.
The energy per band i for zero lines (EZ
The EZ
The values of EB
The block LTP will be explained now. The time-domain signal yC is used as the input to the LTP, where yC is obtained from XC as output of IMDCT. IMDCT consists of the inverse MDCT, windowing and the Overlap-and-Add. The left overlap part and the non-overlapping part of yC in the current frame is saved in the LTP buffer.
The LTP buffer is used in the following frame in the LTP to produce the predicted signal for the whole window of the MDCT. This is illustrated by
If a shorter overlap, for example half overlap, is used for the right overlap in the current window, then also the non-overlapping part “overlap diff” is saved in the LTP buffer. Thus, the samples at the position “overlap diff” (cf.
If a shorter overlap is used for the left overlap in the current window, the whole non-overlapping part up to the start of the current window is used as a part of the LTP buffer for producing the predicted signal.
The predicted signal for the whole window of the MDCT is produced from the LTP buffer. The time interval of the window length is split into overlapping sub-intervals of length LsubF0 with the hop size LupdateF0=LsubF0/2. Other hop sizes and relations between the sub-interval length and the hop size may be used. The overlap length may be LupdateF0−LsubF0 or smaller. LsubF0 is chosen so that no significant pitch change is expected within the sub-intervals. In an example LupdateF0 is an integer closest to
Below, an example of “calculation means (1030) configured to derive sub-interval parameters from the encoded pitch parameter dependent on a position of the sub-intervals within the interval associated with the frame of the encoded audio signal” and also an example of “parameters are derived from the encoded pitch parameter and the sub-interval position within the interval associated with the frame of the encoded audio signal” will be given. For each sub-interval pitch lag at the center of the sub-interval isubCenter is obtained from the pitch contour. In the first step, the sub-interval pitch lag dsubF0 is set to the pitch lag at the position of the sub-interval center dcontour[isubCenter]. As long as the distance of the sub-interval end to the window start (isubCenter+LsubF0/2) is bigger than dsubF0, dsubF0 is increased for the value of the pitch lag from the pitch contour at position dsubF0 to the left of the sub-interval center, that is dsubF0=dsubF0+dcontour[isubCenter−dsubF0] until isubCenter+LsubF0/2<dsubF0. The distance of the sub-interval end to the window start (isubCenter+LsubF0/2) may also be termed the sub-interval end.
In each sub-interval the predicted signal is constructed using the LTP buffer and a filter with the transfer function HLTP(z), where:
HLTP(z)=B(z,Tfr)z−T
where Tint is the integer part of dsubF0, that is Tint=└dsubF0┘, and Tfr is the fractional part of dsubF0, that is Tfr=dsubF0−Tint, and B(z,Tfr) is a fractional delay filter. B(z,Tfr) may have a low-pass characteristics (or it may de-emphasize the high frequencies). The prediction signal is then cross-faded in the overlap regions of the sub-intervals. HLTP2(z) Alternatively the predicted signal can be constructed using the method with cascaded filters as described in [21], with zero input response (ZIR) of a filter based on the
filter with the transfer function and the LTP buffer used as the initial output of the filter, where:
Examples for B(z,Tfr):
B(z, 0/4=0.0000z−2+0.2325z−1+0.5349z0+0.2325z1
B(z,¼=0.0152z−2+0.3400z−1+0.5094z0+0.1353z1
B(z, 2/4=0.0609z−2+0.4391z−1+0.4391z0+0.0609z1
B(z,¾=0.1353z−2+0.5094z−1+0.3400z0+0.0152z1
In the examples Tfr is usually rounded to the nearest value from a list of values and for each value in the list the filter B is predefined.
The predicted signal XP′ is windowed, with the same window as the window used to produce XM, and transformed via MDCT to obtain XP.
Below, an example of means for modifying the predicted spectrum, or a derivative of the predicted spectrum, dependent on a parameter derived from the encoded pitch parameter will be given. The magnitudes of the MDCT coefficients at least nFsafeguard away from the harmonics in XP are set to zero (or multiplied with a positive factor smaller than 1), where NFsafeguard is for example 10. Alternatively other windows than the rectangular window may be used to reduce the magnitudes between the harmonics. It is considered that the harmonics in XP are at bin locations that are integer multiples of iF0=2LM/
The spectral envelope of XP is perceptually flattened with the same method as XM, for example via SNSE, to obtain XPS.
Below an example of “a number of predictable harmonics is determined based on the coded pitch parameter is given. Using XPS, XMS and
If the LTP is active then first └(nLTP+0.5)iF0┘ coefficients of XPS, except the zeroth coefficient, are subtracted from XMT to produce XMR. The zeroth and the coefficients above └(nLTP+0.5)iF0┘ are copied from XMT to XMR.
In a process of a quantization, XQ is obtained from XMR, and XQ is coded as spect, and by decoding XD is obtained from spect.
Below, an example of a combiner (157) configured to combine at least a portion of the prediction spectrum (XP) or a portion of the derivative of the predicted spectrum (XPS) with the error spectrum (XD) will be given. If the LTP is active then first └(nLTP+0.5)iF0┘ coefficients of XPS, except the zeroth coefficient, are added to XP to produce XDT. The zeroth and the coefficients above └(nLTP+0.5)iF0┘ are copied from XD to XDT.
Below, the optional features of harmonic post-filtering will be discussed.
A time-domain signal yC is obtained from XC as output of IMDCT where IMDCT consists of the inverse MDCT, windowing and the Overlap-and-Add. A harmonic post-filter (HPF) that follows pitch contour is applied on yC to reduce noise between harmonics and to output yH. Instead of yC, a combination of yC and a time domain signal yP, constructed from the decoded pulse waveforms, may be used as the input to the HPF. As illustrated by
The HPF input for the current frame k is yC[n](0≤n<N). The past output samples yH[n] (−dHPFmax≤n<0, where dHPFmax is at least the maximum pitch lag) are also available. Nahead IMDCT look-ahead samples are also available, that may include time aliased portions of the right overlap region of the inverse MDCT output. We show an example where an time interval on which HPF is applied is equal to the current frame, but different intervals may be used. The location of the HPF current input/output, the HPF past output and the IMDCT look-ahead relative to the MDCT/IMDCT windows is illustrated by
If it is signaled in the bit-stream that the HPF should use constant parameters, a smoothing is used at the beginning of the current frame, followed by the HPF with constant parameters on the remaining of the frame. Alternatively, a pitch analysis may be performed on yC to decide if constant parameters should be used. The length of the region where the smoothing is used may be dependent on pitch parameters.
When constant parameters are not signaled, the HPF input is split into overlapping sub-intervals of length Lk with the hop size Lk,update=Lk/2. Other hop sizes may be used. The overlap length may be Lk,update−Lk or smaller. Lk is chosen so that no significant pitch change is expected within the sub-intervals. In an example Lk,update is an integer closest to pitch_mid/2, but not greater than pitch_mid/2, and Lk is set to 2Lk,update. Instead of pitch_mid some other values may be used, for example mean of pitch_mid and pitch_start or a value obtained from a pitch analysis on yC or for example an expected minimum pitch lag in the interval for signals with varying pitch. Alternatively a fixed number of sub-intervals may be chosen. In another example it may be additionally requested that the frame length is divisible by Lk,update (cf.
We say that the number of sub-intervals in the current interval k is Kk, in the previous interval k−1 is Kk−1 and in the following interval k+1 is Kk+1. In the example in
In other example it is possible that the current (time) interval is split into non integer number of sub-intervals and/or that the length of the sub-intervals change within the current interval as shown below. This is illustrated by
For each sub-interval l in the current interval k (1≤l≤Kk), sub-interval pitch lag pk,l is found using a pitch search algorithm, which may be the same as the pitch search used for obtaining the pitch contour or different from it. The pitch search for sub-interval l may use values derived from the coded pitch lag (pitch_mid, pitch_end) to reduce the complexity of the search and/or to increase the stability of the values pk,l across the sub-intervals, for example the values derived from the coded pitch lag may be the values of the pitch contour. In other example, parameters found by a global pitch analysis in the complete interval of yC may be used instead of the coded pitch lag to reduce the complexity of the search and/or the stability of the values pk,l across the sub-intervals. In another example, when searching for the sub-interval pitch lag, it is assumed that an intermediate output of the harmonic post-filtering for previous sub-intervals is available and used in the pitch search (including sub-intervals of the previous intervals).
The Nahead (potentially time aliased) look-ahead samples may also be used for finding pitch in sub-intervals that cross the interval/frame border or, for example if the look-ahead is not available, a delay may be introduced in the decoder in order to have look-ahead for the last sub-interval in the interval. Alternatively a value derived from the coded pitch lag (pitch_mid, pitch_end) may be used for pk,K
For the harmonic post-filtering, the gain adaptive harmonic post-filter may be used. In the example the HPF has the transfer function:
where B(z,Tfr) is a fractional delay filter. B(z,Tfr) may be the same as the fractional delay filters used in the LTP or different from them, as the choice is independent. In the HPF, B(z,Tfr) acts also as a low-pass (or a tilt filter that de-emphasizes the high frequencies). An example for the difference equation for the gain adaptive harmonic post-filter with the transfer function H(z) and bj(Tfr) as coefficients of B(z,Tfr) is:
Instead of a low-pass filter with a fractional delay, the identity filter may be used, giving B(z,Tfr)=1 and the difference equation:
y[n]=x[n]−βh(αx[n]−gy[n−Tint])
The parameter g is the optimal gain. It models the amplitude change (modulation) of the signal and is signal adaptive.
The parameter h is the harmonicity level. It controls the desired increase of the signal harmonicity and is signal adaptive. The parameter β also controls the increase of the signal harmonicity and is constant or dependent on the sampling rate and bit-rate. The parameter β may also be equal to 1. The value of the product βh should be between 0 and 1, 0 producing no change in the harmonicity and 1 maximally increasing the harmonicity. In practice it is usual that βh<0.75.
The feed-forward part of the harmonic post-filter (that is 1−αβhB (z,0)) acts as a high-pass (or a tilt filter that de-emphasizes the low frequencies). The parameter α determines the strength of the high-pass filtering (or in another words it controls the de-emphasis tilt) and has value between 0 and 1. The parameter α is constant or dependent on the sampling rate and bit-rate. Value between 0.5 and 1 is of advantage in embodiments.
For each sub-interval, optimal gain gk,l and harmonicity level hk,l is found or in some cases it could be derived from other parameters.
For a given B(z,Tfr) we define a function for shifting/filtering a signal as:
With these definitions yL,l[n] represents for 0≤n<L the signal yC in a (sub-)interval l with length L,
We define normalized correlation normcorr(yC,yH,l,L,p) of signals yC and yH at (sub-)interval l with length L and shift p as:
An alternative definition of normcorr(yC,yH,l,L,p) may be:
In the alternative definition yL,l[n−Tint] represents yH in the past sub-intervals for n<Tint.
In the definitions above we have used the 4th order B(z,Tfr). Any other order may be used, requiring change in the range for j. In the example where B(z,Tfr)=1, we get
The normalized correlation defined in this manner allows calculation for fractional shifts p.
The parameters of normcorr l and L define the window for the normalized correlation. In the above definition rectangular window is used. Any other type of window (e.g. Hann, Cosine) may be used instead which can be done multiplying
To get the normalized correlation on a sub-interval we would set l to the interval number and L to the length of the sub-interval.
The output of yL,l−p[n] represents the ZIR of the gain adaptive harmonic post-filter H(z) for the sub-frame l, with β=h=g=1 and Tint=└p┘ and Tfr=p−Tint.
The optimal gain gk,l models the amplitude change (modulation) in the sub-frame l. It may be for example calculated as a correlation of the predicted signal with the low passed input divided by the energy of the predicted signal:
In another example the optimal gain gk,l may be calculated as the energy of the low passed input divided by the energy of the predicted signal:
The harmonicity level hk,l controls the desired increase of the signal harmonicity and can be for example calculated as square of the normalized correlation:
h
k,l=normcorr(yC,yH,l,Lk,pk,l)2
Usually the normalized correlation of a sub-interval is already available from the pitch search at the sub-interval.
The harmonicity level hk,l may also be modified depending on the LTP and/or depending on the decoded spectrum characteristics. For an example we may set:
h
k,l
=h
modLTP
h
modTiltnormcorr(yC,yH,l,Lk,pk,l)2
where hmodLTP is a value between 0 and 1 and proportional to the number of harmonics predicted by the LTP and hmodTilt is a value between 0 and 1 and inverse proportional to a tilt of XC. In an example hmodLTP=0.5 if nLTP is zero, otherwise hmodLTP=0.7+0.3nLTP/NLTP. The tilt of XC may be the ratio of the energy of the first 7 spectral coefficients to the energy of the following 43 coefficients.
Once we have calculated the parameters for the sub-interval l, we can produce the intermediate output of the harmonic post-filtering for the part of the sub-interval l that is not overlapping with the sub-interval l+1. As written above, this intermediate output is used in finding the parameters for the subsequent sub-intervals.
Each sub-interval is overlapping and a smoothing operation between two filter parameters is used. The smoothing as described in [3] may be used.
Below, advantageous embodiments will be discussed
According to embodiments, an apparatus for encoding an audio signal is provided the apparatus comprises the following entities:
Another embodiment provides an apparatus for encoding an audio signal which, vice versa comprises the following entities:
According to embodiments, both apparatuses may be enhanced by a modifier that adaptively sets to zero at least a sub-band in the quantized spectrum, depending on the content of the sub-band in the quantized spectrum and in the perceptually flattened spectral representation.
Here a two-step band-wise parametric coder may be used. The two step band-wise parametric coder is configured for providing a parametric representation of the perceptually flattened spectral representation, or a derivative of the perceptually flattened spectral representation, depending on the quantization step, for sub-bands where the quantized spectrum is zero(, so that at least two sub-bands have different parametric representation);
Another embodiment provides an apparatus for decoding an encoded audio signal. The apparatus for decoding comprises the following entities:
Another embodiment provides a band-wise parametric spectrum generator providing a generated spectrum that is combined with the decoded spectrum; or
Note the source spectrum may, according to further embodiments, be weighted based on energy parameters of zero sub-bands. The choice of the source spectrum for a sub-band is dependent on the sub-band positon, a power spectrum estimate, energy parameters, pitch information and temporal information.
According to embodiments, a number of parameters describing the spectral representation (XMR) may depend on the quantized representation (XQ).
Note in yet another embodiment, sub-bands (that is sub-band borders) for the iBPC, “zfl decode” and “Zero Filling” could be derived from the positions of the zero spectral coefficients in XD and/or XQ.
Although some aspects have been described in the context of an apparatus, it is clear that these aspects also represent a description of the corresponding method, where a block or device corresponds to a method step or a feature of a method step. Analogously, aspects described in the context of a method step also represent a description of a corresponding block or item or feature of a corresponding apparatus. Some or all of the method steps may be executed by (or using) a hardware apparatus, like for example, a microprocessor, a programmable computer or an electronic circuit. In some embodiments, some one or more of the most important method steps may be executed by such an apparatus.
The inventive encoded audio signal can be stored on a digital storage medium or can be transmitted on a transmission medium such as a wireless transmission medium or a wired transmission medium such as the Internet.
Depending on certain implementation requirements, embodiments of the invention can be implemented in hardware or in software. The implementation can be performed using a digital storage medium, for example a floppy disk, a DVD, a Blu-Ray, a CD, a ROM, a PROM, an EPROM, an EEPROM or a FLASH memory, having electronically readable control signals stored thereon, which cooperate (or are capable of cooperating) with a programmable computer system such that the respective method is performed. Therefore, the digital storage medium may be computer readable.
Some embodiments according to the invention comprise a data carrier having electronically readable control signals, which are capable of cooperating with a programmable computer system, such that one of the methods described herein is performed.
Generally, embodiments of the present invention can be implemented as a computer program product with a program code, the program code being operative for performing one of the methods when the computer program product runs on a computer. The program code may for example be stored on a machine readable carrier.
Other embodiments comprise the computer program for performing one of the methods described herein, stored on a machine readable carrier.
In other words, an embodiment of the inventive method is, therefore, a computer program having a program code for performing one of the methods described herein, when the computer program runs on a computer.
A further embodiment of the inventive methods is, therefore, a data carrier (or a digital storage medium, or a computer-readable medium) comprising, recorded thereon, the computer program for performing one of the methods described herein. The data carrier, the digital storage medium or the recorded medium are typically tangible and/or non-transitionary.
A further embodiment of the inventive method is, therefore, a data stream or a sequence of signals representing the computer program for performing one of the methods described herein. The data stream or the sequence of signals may for example be configured to be transferred via a data communication connection, for example via the Internet.
A further embodiment comprises a processing means, for example a computer, or a programmable logic device, configured to or adapted to perform one of the methods described herein.
A further embodiment comprises a computer having installed thereon the computer program for performing one of the methods described herein.
A further embodiment according to the invention comprises an apparatus or a system configured to transfer (for example, electronically or optically) a computer program for performing one of the methods described herein to a receiver. The receiver may, for example, be a computer, a mobile device, a memory device or the like. The apparatus or system may, for example, comprise a file server for transferring the computer program to the receiver.
In some embodiments, a programmable logic device (for example a field programmable gate array) may be used to perform some or all of the functionalities of the methods described herein. In some embodiments, a field programmable gate array may cooperate with a microprocessor in order to perform one of the methods described herein. Generally, the methods may be performed by any hardware apparatus.
While this invention has been described in terms of several embodiments, there are alterations, permutations, and equivalents which will be apparent to others skilled in the art and which fall within the scope of this invention. It should also be noted that there are many alternative ways of implementing the methods and compositions of the present invention. It is therefore intended that the following appended claims be interpreted as including all such alterations, permutations, and equivalents as fall within the true spirit and scope of the present invention.
| Number | Date | Country | Kind |
|---|---|---|---|
| 21185666.1 | Jul 2021 | EP | regional |
This application is a continuation of copending International Application No. PCT/EP2022/069811, filed Jul. 14, 2022, which is incorporated herein by reference in its entirety, and additionally claims priority from European Application No. 21185666.1, filed Jul. 14, 2021, which is also incorporated herein by reference in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/EP2022/069811 | Jul 2022 | WO |
| Child | 18405402 | US |
