Patent Application
20110257965
This description relates generally to the encoding and/or decoding of speech and other audio signals
Speech encoding and decoding have a large number of applications and have been studied extensively. In general, speech coding, which is also known as speech compression, seeks to reduce the data rate needed to represent a speech signal without substantially reducing the quality or intelligibility of the speech. Speech compression techniques may be implemented by a speech coder, which also may be referred to as a voice coder or vocoder.
A speech coder is generally viewed as including an encoder and a decoder. The encoder produces a compressed stream of bits from a digital representation of speech, such as may be generated at the output of an analog-to-digital converter having as an input an analog signal produced by a microphone. The decoder converts the compressed bit stream into a digital representation of speech that is suitable for playback through a digital-to-analog converter and a speaker. In many applications, the encoder and the decoder are physically separated, and the bit stream is transmitted between them using a communication channel.
A key parameter of a speech coder is the amount of compression the coder achieves, which is measured by the bit rate of the stream of bits produced by the encoder. The bit rate of the encoder is generally a function of the desired fidelity (i.e., speech quality) and the type of speech coder employed. Different types of speech coders have been designed to operate at different bit rates. Recently, low-to-medium rate speech coders operating below 10 kbps have received attention with respect to a wide range of mobile communication applications (e.g., cellular telephony, satellite telephony, land mobile radio, and in-flight telephony). These applications typically require high quality speech and robustness to artifacts caused by acoustic noise and channel noise (e.g., bit errors).
Speech is generally considered to be a non-stationary signal having signal properties that change over time. This change in signal properties is generally linked to changes made in the properties of a person's vocal tract to produce different sounds. A sound is typically sustained for some short period, typically 10-100 ms, and then the vocal tract is changed again to produce the next sound. The transition between sounds may be slow and continuous, or the transition may be rapid as in the case of a speech “onset.” This change in signal properties increases the difficulty of encoding speech at lower bit rates since some sounds are inherently more difficult to encode than others and the speech coder must be able to encode all sounds with reasonable fidelity while preserving the ability to adapt to a transition in characteristics of the speech signal. One way to improve the performance of a low-to-medium bit rate speech coder is to allow the bit rate to vary. In variable-bit-rate speech coders, the bit rate for each segment of speech is not fixed, and, instead, is allowed to vary between two or more options depending on various factors, such as user input, system loading, terminal design or signal characteristics.
There have been several main approaches for coding speech at low-to-medium data rates. For example, an approach based around linear predictive coding (LPC) attempts to predict each new frame of speech from previous samples using short and long term predictors. The prediction error is typically quantized using one of several approaches of which CELP and/or multi-pulse are two examples. An advantage of the LPC method is that it has good time resolution, which is helpful for the coding of unvoiced sounds. In particular, plosives and transients benefit from this in that they are not overly smeared in time. However, linear prediction may have difficulty for voiced sounds in that the coded speech tends to sound rough or hoarse due to insufficient periodicity in the coded signal. This problem may be more significant at lower data rates that typically require a longer frame size and for which the long-term predictor is less effective at restoring periodicity.
Another leading approach for low-to-medium rate speech coding is a model-based speech coder or vocoder. A vocoder models speech as the response of a system to excitation over short time intervals. Examples of vocoder systems include linear prediction vocoders (e.g., MELP), homomorphic vocoders, channel vocoders, sinusoidal transform coders (“STC”), harmonic vocoders and multiband excitation (“MBE”) vocoders. In these vocoders, speech is divided into short segments (typically 10-40 ms), with each segment being characterized by a set of model parameters. These parameters typically represent a few basic elements of each speech segment, such as the pitch, voicing state, and spectral envelope of the segment. A vocoder may use one of a number of known representations for each of these parameters. For example, the pitch may be represented as a pitch period, a fundamental frequency or pitch frequency (which is the inverse of the pitch period), or as a long-term prediction delay. Similarly, the voicing state may be represented by one or more voicing metrics, by a voicing probability measure, or by a set of voicing decisions. The spectral envelope is often represented by an all-pole filter response, but also may be represented by a set of spectral magnitudes or other spectral measurements. Since model-based speech coders permit a speech segment to be represented using only a small number of parameters, model-based speech coders, such as vocoders, typically are able to operate at medium to low data rates. However, the quality of a model-based system is dependent on the accuracy of the underlying model. Accordingly, a high fidelity model must be used if these speech coders are to achieve high speech quality.
The MBE vocoder is a harmonic vocoder based on the MBE speech model that has been shown to work well in many applications. The MBE vocoder combines a harmonic representation for voiced speech with a flexible, frequency-dependent voicing structure based on the MBE speech model. This allows the MBE vocoder to produce natural sounding unvoiced speech and makes the MBE vocoder more robust to the presence of acoustic background noise. These properties allow the MBE vocoder to produce higher quality speech at low to medium data rates and have led to use of the MBE vocoder in a number of commercial mobile communication applications.
The MBE speech model represents segments of speech using a fundamental frequency corresponding to the pitch, a set of voicing metrics or decisions, and a set of spectral magnitudes corresponding to the frequency response of the vocal tract. The MBE model generalizes the traditional single V/UV decision per segment into a set of decisions, each representing the voicing state within a particular frequency band or region. Each frame is thereby divided into at least voiced and unvoiced frequency regions. This added flexibility in the voicing model allows the MBE model to better accommodate mixed voicing sounds, such as some voiced fricatives, allows a more accurate representation of speech that has been corrupted by acoustic background noise, and reduces the sensitivity to an error in any one decision. Extensive testing has shown that this generalization results in improved voice quality and intelligibility.
MBE-based vocoders include the IMBE™ speech coder and the AMBE® speech coder. The IMBE™ speech coder has been used in a number of wireless communications systems including APCO Project 25. The AMBE® speech coder is an improved system which includes a more robust method of estimating the excitation parameters (fundamental frequency and voicing decisions), and which is better able to track the variations and noise found in actual speech. Typically, the AMBE® speech coder uses a filter bank that often includes sixteen channels and a non-linearity to produce a set of channel outputs from which the excitation parameters can be reliably estimated. The channel outputs are combined and processed to estimate the fundamental frequency. Thereafter, the channels within each of several (e.g., eight) voicing bands are processed to estimate a voicing decision (or other voicing metrics) for each voicing band. In the AMBE+2™ vocoder, a three-state voicing model (voiced, unvoiced, pulsed) is applied to better represent plosive and other transient speech sounds. Various methods for quantizing the MBE model parameters have been applied in different systems. Typically the AMBE® vocoder and AMBE+2™ vocoder employ more advanced quantization methods, such as vector quantization, that produce higher quality speech at lower bit rates.
The encoder of an MBE-based speech coder estimates the set of model parameters for each speech segment. The MBE model parameters include a fundamental frequency (the reciprocal of the pitch period); a set of V/UV metrics or decisions that characterize the voicing state; and a set of spectral magnitudes that characterize the spectral envelope. After estimating the MBE model parameters for each segment, the encoder quantizes the parameters to produce a frame of bits. The encoder optionally may protect these bits with error correction/detection codes before interleaving and transmitting the resulting bit stream to a corresponding decoder.
The decoder in an MBE-based vocoder reconstructs the MBE model parameters (fundamental frequency, voicing information and spectral magnitudes) for each segment of speech from the received bit stream. As part of this reconstruction, the decoder may perform deinterleaving and error control decoding to correct and/or detect bit errors. In addition, phase regeneration is typically performed by the decoder to compute synthetic phase information. In one method, which is specified in the APCO Project 25 Vocoder Description and described in U.S. Pat. Nos. 5,081,681 and 5,664,051, random phase regeneration is used, with the amount of randomness depending on the voicing decisions.
In another method, phase regeneration is performed by applying a smoothing kernel to the reconstructed spectral magnitudes as is described in U.S. Pat. No. 5,701,390. The decoder uses the reconstructed MBE model parameters to synthesize a speech signal that perceptually resembles the original speech to a high degree. Normally separate signal components, corresponding to voiced, unvoiced, and optionally pulsed speech, are synthesized for each segment, and the resulting components are then added together to form the synthetic speech signal. This process is repeated for each segment of speech to reproduce the complete speech signal for output through a D-to-A converter and a loudspeaker. The unvoiced signal component may be synthesized using a windowed overlap-add method to filter a white noise signal. The time-varying spectral envelope of the filter is determined from the sequence of reconstructed spectral magnitudes in frequency regions designated as unvoiced, with other frequency regions being set to zero.
The decoder may synthesize the voiced signal component using one of several methods. In one method, specified in the APCO Project 25 Vocoder Description, a bank of harmonic oscillators is used, with one oscillator assigned to each harmonic of the fundamental frequency, and the contributions from all of the oscillators are summed to form the voiced signal component. In another method, the voiced signal component is synthesized by convolving a voiced impulse response with an impulse sequence and then combining the contribution from neighboring segments with windowed overlap add. This second method may be faster to compute, since it does not require any matching of components between segments, and it may be applied to the optional pulsed signal component.
One particular example of an MBE based vocoder is the 7200 bps IMBE™ vocoder selected as a standard for the APCO Project 25 mobile radio communication system. This vocoder, described in the APCO Project 25 Vocoder Description, uses 144 bits to represent each 20 ms frame. These bits are divided into 56 redundant FEC bits (applied by a combination of Golay and Hamming coding), 1 synchronization bit and 87 MBE parameter bits. The 87 MBE parameter bits consist of 8 bits to quantize the fundamental frequency, 3-12 bits to quantize the binary voiced/unvoiced decisions, and 67-76 bits to quantize the spectral magnitudes. The resulting 144 bit frame is transmitted from the encoder to the decoder. The decoder performs error correction before reconstructing the MBE model parameters from the error decoded bits. The decoder then uses the reconstructed model parameters to synthesize voiced and unvoiced signal components which are added together to form the decoded speech signal.
In one general aspect, encoding a sequence of digital speech samples into a bit stream includes dividing the digital speech samples into one or more frames and computing model parameters for multiple frames. The model parameters include at least a first parameter conveying pitch information. A voicing state of a frame is determined, and the parameter conveying pitch information for the frame is modified to designate the determined voicing state of the frame if the determined voicing state of the frame is equal to one of a set of reserved voicing states. The model parameters then are quantized to generate quantizer bits used to produce the bit stream.
Implementations may include one or more of the following features. For example, the model parameters may further include one or more spectral parameters determining spectral magnitude information.
The voicing state of a frame may be determined for multiple frequency bands, and the model parameters may further include one or more voicing parameters that designate the determined voicing state in the frequency bands. The voicing parameters may designate the voicing state in each frequency band as either voiced, unvoiced or pulsed. The set of reserved voicing states may correspond to voicing states where no frequency band is designated as voiced. The voicing parameters may be set to designate all frequency bands as unvoiced if the determined voicing state of the frame is equal to one of a set of reserved voicing states. The voicing state also may be set to designate all frequency bands as unvoiced if the frame corresponds to background noise rather than to voice activity.
Producing the bit stream may include applying error correction coding to the quantizer bits. The produced bit stream may be interoperable with a standard vocoder used for APCO Project 25.
A frame of digital speech samples may be analyzed to detect tone signals, and, if a tone signal is detected, the set of model parameters for the frame may be selected to represent the detected tone signal. The detected tone signals may include DTMF tone signals. Selecting the set of model parameters to represent the detected tone signal may include selecting the spectral parameters to represent the amplitude of the detected tone signal and/or selecting the first parameter conveying pitch information based at least in part on the frequency of the detected tone signal.
The spectral parameters that determine spectral magnitude information for the frame include a set of spectral magnitude parameters computed around harmonics of a fundamental frequency determined from the first parameter conveying pitch information.
In another general aspect, encoding a sequence of digital speech samples into a bit stream includes dividing the digital speech samples into one or more frames and determining whether the digital speech samples for a frame correspond to a tone signal. Model parameters are computed for multiple frames, with the model parameters including at least a first parameter representing the pitch and spectral parameters representing the spectral magnitude at harmonic multiples of the pitch. If the digital speech samples for a frame are determined to correspond to a tone signal, the pitch parameter and the spectral parameters are selected to approximate the detected tone signal. The model parameters are quantized to generate quantizer bits which are used to produce the bit stream.
Implementations may include one or more of the following features and one or more of the features noted above. For example, the set of model parameters may further include one or more voicing parameters that designate the voicing state in multiple frequency bands. The first parameter representing the pitch may be the fundamental frequency.
In another general aspect, decoding digital speech samples from a sequence of bits, includes dividing the sequence of bits into individual frames that each include multiple bits. Quantizer values are formed from a frame of bits. The formed quantizer values include at least a first quantizer value representing the pitch and a second quantizer value representing the voicing state. A determination is made as to whether the first and second quantizer values belong to a set of reserved quantizer values. Thereafter, speech model parameters are reconstructed for a frame from the quantizer values. The speech model parameters represent the voicing state of the frame being reconstructed from the first quantizer value representing the pitch if the first and second quantizer values are determined to belong to the set of reserved quantizer values. Finally, digital speech samples are computed from the econstructed speech model parameters.
Implementations may include one or more of the following features and one or more of the features noted above. For example, the reconstructed speech model parameters for a frame may include a pitch parameter and one or more spectral parameters representing the spectral magnitude information for the frame. A frame may be divided into frequency bands and the reconstructed speech model parameters representing the voicing state of a frame may designate the voicing state in each of the frequency bands. The voicing state in each frequency band may be designated as either voiced, unvoiced or pulsed. The bandwidth of one or more of the frequency bands may be related to the pitch frequency.
The first and second quantizer values may be determined to belong to the set of reserved quantizer values only if the second quantizer value equals a known value. The known value may be the value designating all frequency bands as unvoiced. The first and second quantizer values may be determined to belong to the set of reserved quantizer values only if the first quantizer value equals one of several permissible values. The voicing state in each frequency band may not be designated as voiced if the first and second quantizer values are determined to belong to the set of reserved quantizer values.
Forming the quantizer values from a frame of bits may include performing error decoding on the frame of bits. The sequence of bits may be produced by a speech encoder which is interoperable with the APCO Project 25 vocoder standard.
The reconstructed spectral parameters may be modified if the reconstructed speech model parameters for a frame are determined to correspond to a tone signal. Modifying the reconstructed spectral parameters may include attenuating certain undesired frequency components. The reconstructed model parameters for a frame may be determined to correspond to a tone signal only if the first quantizer value and the second quantizer value are equal to certain known tone quantizer values or if the spectral magnitude information for a frame indicates a small number of dominant frequency components. The tone signals may include DTMF tone signals which are determined only if the spectral magnitude information for a frame indicates two dominant frequency components occurring at or near the known DTMF frequencies.
The spectral parameters representing the spectral magnitude information for the frame may consist of a set of spectral magnitude parameters representing harmonics of a fundamental frequency determined from the reconstructed pitch parameter.
In another general aspect, decoding digital speech samples from a sequence of bits includes dividing the sequence of bits into individual frames that each contain multiple bits. Speech model parameters are reconstructed from a frame of bits. The reconstructed speech model parameters for a frame include one or more spectral parameters representing the spectral magnitude information for the frame. Using the reconstructed speech model parameters, a determination is made as to whether the frame represents a tone signal, and the spectral parameters are modified if the frame represents a tone signal, such that the modified spectral parameters better represent the spectral magnitude information of the determined tone signal. Digital speech samples are generated from the reconstructed speech model parameters and the modified spectral parameters.
Implementations may include one or more of the following features and one or more of the features noted above. For example, the reconstructed speech model parameters for a frame also include a fundamental frequency parameter representing the pitch and voicing parameters that designate the voicing state in multiple frequency bands. The voicing state in each of the frequency bands may be designated as either voiced, unvoiced or pulsed.
The spectral parameters for the frame may include a set of spectral magnitudes representing the spectral magnitude information at harmonics of the fundamental frequency parameter. Modifying the reconstructed spectral parameters may include attenuating the spectral magnitudes corresponding to harmonics which are not contained in the determined tone signal.
The reconstructed speech model parameters for a frame may be determined to correspond to a tone signal only if a few of the spectral magnitudes in the set of spectral magnitudes are dominant over all the other spectral magnitudes in the set, or if the fundamental frequency parameter and the voicing parameters are approximately equal to certain known values for the parameters. The tone signals may include DTMF tone signals which are determined only if the set of spectral magnitudes contain two dominant frequency components occurring at or near the standard DTMF frequencies.
The sequence of bits may be produced by a speech encoder which is interoperable with the APCO Project 25 vocoder standard.
In another general aspect, an enhanced Multi-Band Excitation (MBE) vocoder is interoperable with the standard APCO Project 25 vocoder but provides improved voice quality, better fidelity for tone signals and improved robustness to background noise. An enhanced MBE encoder unit may include elements such as MBE parameter estimation, MBE parameter quantization and FEC encoding. The MBE parameter estimation element includes advanced features such as voice activity detection, noise suppression, tone detection, and a three-state voicing model. MBE parameter quantization includes the ability to insert voicing information in the fundamental frequency data field. An enhanced MBE decoder may include elements such as FEC decoding, MBE parameter reconstruction and MBE speech synthesis. MBE parameter reconstruction features the ability to extract voicing information from the fundamental frequency data field. MBE speech synthesis may synthesize speech as a combination of voiced, unvoiced and pulsed signal components.
Other features will be apparent from the following description, including the drawings, and the claims.
Typically, the speech encoder processes the digital speech signal in short frames, where the frames may be further divided into one or more subframes. Each frame of digital speech samples produces a corresponding frame of bits in the bit stream output of the encoder. Note that if there is only one subframe in the frame, then the frame and subframe typically are equivalent and refer to the same partitioning of the signal. In one implementation, the frame size is 20 ms in duration and consists of 160 samples at a 8 kHz sampling rate. Performance may be increased in some applications by dividing each frame into two 10 ms subframes.
The vocoder 100 is an enhanced MBE-based vocoder that is interoperable with the standard vocoder used in the APCO Project 25 communication system. In one implementation, an enhanced 7200 bps vocoder is interoperable with the standard APCO Project 25 vocoder bit stream. This enhanced 7200 bps vocoder provides improved performance, including better voice quality, increased immunity to acoustic background noise, and superior tone handling. Bit stream interoperability is preserved so that an enhanced encoder produces a 7200 bps bit stream which can be decoded by a standard APCO Project 25 voice decoder to produce high quality speech. Similarly, the enhanced decoder inputs and decodes high quality speech from a 7200 bps bit stream generated by a standard encoder. The provision for bit stream interoperability allows radios or other devices incorporating the enhanced vocoder to be seamlessly integrated into the existing APCO Project 25 system, without requiring conversion or transcoding by the system infrastructure. By providing backward compatibility with the standard vocoder, the enhanced vocoder can be used to upgrade the performance of the existing system without introducing interoperability problems.
Referring to
As also shown in
In the APCO Project 25 vocoder standard, 144 bits are used to represent each 20 ms frame. These bits are divided into 56 redundant FEC bits (applied by a combination of Golay and Hamming coding), 1 synchronization bit, and 87 MBE parameter bits. To be interoperable with the standard APCO Project 25 vocoder bit stream, the enhanced vocoder uses the same frame size and the same general bit allocation within each frame. However, the enhanced vocoder employs certain modification to these bits, relative to the standard vocoder, to convey extra information and to improve vocoder performance, while remaining backward compatible with the standard vocoder.
The voice encoder also performs voice activity detection (VAD) (step 310) to determine, for each frame, whether the input signal is human voice or background noise. The output of the VAD is a single bit of information per frame designating the frame as voice or no voice.
The encoder then estimates the MBE voicing decisions and the fundamental frequency, which conveys pitch information (step 315), and the spectral magnitudes (step 320). The voicing decisions may be set to all unvoiced if the VAD decision determines the frame to be background noise (no voice).
After the spectral magnitudes are estimated, noise suppression is applied (step 325) to remove the perceived level of background noise from the spectral magnitudes. In some implementations, the VAD decision is used to improve the background noise estimate.
Finally, the spectral magnitudes are compensated (step 330) if they are in a voicing band designated as unvoiced or pulsed. This is done to account for the different spectral magnitude estimation method used in the standard vocoder.
The enhanced MBE voice encoder performs tone detection to identify certain types of tone signals in the input signal.
Next, the best candidate tone is determined, generally by finding the FFT bin or bins with maximum energy (step 420). The tone energy then is computed by summing the FFT bins around the selected candidate tone frequency in the case of single tone, or frequencies in the case of a dual tone (step 425).
The candidate tone is then validated by checking certain tone parameters, such as the SNR (ratio between tone energy and total energy) level, frequency, or twist (step 430). For example, in the case of DTMF tones, which are standardized dual frequency tones used in telecommunications, the frequency of each of the two frequency components must be within about 3% of the nominal value for a valid DTMF tone, and the SNR must typically exceed 15 dB. If such tests confirm a valid tone, then the estimated tone parameters are mapped to a harmonic series using a set of MBE model parameters such as are shown in Table 1 (step 435). For example, a 697 Hz, 1336 Hz DTMF tone may be mapped to a harmonic series with a fundamental frequency of 70 Hz (f0=0.00875) and with two non-zero harmonics (10, 19) and all other harmonics set to zero. The voicing decisions are then set such that the voicing bands containing the non-zero harmonics are voiced, while all other voicing bands are unvoiced.
The enhanced MBE vocoder typically includes voice activity detection (VAD) to identify each frame as either voice or background noise. Various methods for VAD can be applied. However,
Next, an estimate of the background noise floor in each frequency band is estimated by tracking the minimum energy in the band (step 510). The error between the actual measured energy and the estimated noise floor then is computed for each frequency band (step 515) and the error is then accumulated over all the frequency bands (step 520). The accumulated error is then compared against a threshold (step 525), and, if the accumulated error exceeds the threshold, then voice is detected for the frame. If the accumulated error does not exceed the threshold, background noise (no voice) is detected.
The enhanced MBE encoder, shown in
One feature used by the enhanced MBE encoder is that the fundamental frequency is somewhat arbitrary when the frame is entirely unvoiced or pulsed (i.e., has no voiced components). Accordingly, in the case in which no part of the frame is voiced, the fundamental frequency can be used to convey other information, as shown in
Once the channel voicing decisions have been determined, a check is made to determine if any channel is voiced (step 630). If no channel is voiced, then the voicing state for the frame belongs to a set of reserved voicing states where every channel is either unvoiced or pulsed. In this case, the estimated fundamental frequency is replaced with a value from Table 2 (step 635), with the value being selected based on the channel voicing decisions determined in step 625. In addition, if no channel is voiced, then all of the voicing bands used in the standard APCO Project 25 vocoder are set to unvoiced (i.e., b1=0).
The number of voicing bands in a frame, which varies between 3-12 depending on the fundamental frequency, is computed (step 640). The specific number of voicing bands for a given fundamental frequency is described in the APCO Project 25 Vocoder Description and is approximately given by the number of harmonics divided by 3, with a maximum of 12.
If one or more of the channels is voiced, then the voicing state does not belong to the reserved set, the estimated fundamental frequency is maintained and quantized in the standard fashion, and the channel voicing decisions are mapped to the standard APCO Project 25 voicing bands (step 645).
Typically, frequency scaling, from the fixed filterbank channel frequencies to the fundamental frequency dependent voicing band frequencies, is used to perform the mapping shown in step 645.
One selection method is to compare the channel voicing decisions from step 625 with the channel voicing decisions corresponding to each candidate fundamental frequency in Table 2. The table entry for which the channel voicing decisions are closest is selected as the new fundamental frequency and encoded as the fundamental frequency quantizer value, b0. The final part of step 625 is to set the voicing quantizer value, b1, to zero, which normally designates all the voicing bands as unvoiced in the standard decoder. Note that the enhanced encoder sets the voicing quantizer value, b1, to zero whenever the voicing state is a combination of unvoiced and/or pulsed bands in order to ensure that a standard decoder receiving the bit stream produced by the enhanced encoder will decode all the voicing bands as unvoiced. The specific information as to which bands are pulsed and which bands are unvoiced is then encoded in the fundamental frequency quantizer value b0 as described above. The APCO Project 25 Vocoder Description may be consulted for more information on the standard vocoder processing, including the encoding and decoding of the quantizer values b0 and b1.
Note that the channel voicing decisions are normally estimated once per frame, and, in this case, selection of a fundamental frequency from Table 2 involves comparing the estimated channel voicing decisions with the voicing decisions in the Table 2 column labeled “Subframe 1” and using the Table entry which is closest to determine the selected fundamental frequency. In this case, the column of Table 2 labeled “Subframe 0” is not used. However, performance can be further enhanced by estimating the channel voicing decisions twice per frame (i.e., for two subframes in the frame) using the same filterbank-based method described above. In this case, there are two sets of channel voicing decisions per frame, and selection of a fundamental frequency from Table 2 involves comparing the estimated channel voicing decisions for both subframes with the voicing decisions contained in both columns of Table 2. In this case, the Table entry that is closest when examined over both subframes is used to, determine the selected fundamental frequency.
Referring again to
The enhanced MBE encoder typically includes a noise suppression method (step 325) used to reduce the perceived amount of background noise from the estimated spectral magnitudes. One method is to compute an estimate of the local noise floor in a set of frequency bands. Typically, the VAD decision output from voice activity detection (step 310) is used to update the local noise estimated during frames where no voice is detected. This ensures that the noise floor estimate measures the background noise level rather than the speech level. Once the noise estimate is made, the noise estimate is smoothed and then subtracted from the estimated spectral magnitudes using typical spectral subtraction techniques, where the maximum amount of attenuation is typically limited to approximately 15 dB. In cases where the noise estimate is near zero (i.e., there is little or no background noise present), the noise suppression makes little or no change to the spectral magnitudes. However, in cases where substantial noise is present (for example when talking in a vehicle with the windows down), then the noise suppression method makes substantial modification to the estimated spectral magnitudes.
In the standard MBE encoder specified in the APCO Project 25 Vocoder Description, the spectral amplitudes are estimated differently for voiced and unvoiced harmonics. In contrast, the enhanced MBE encoder typically uses the same estimation method, such as described in U.S. Pat. No. 5,754,974, which is incorporated by reference, to estimate all the harmonics. To correct for this difference, the enhanced MBE encoder compensates the unvoiced and pulsed harmonics (i.e., those harmonics in a voicing band declared unvoiced or pulsed) to produce the final spectral magnitudes, Mi as follows:
M
l
=M
l,n
/[K·f
0](1/2) if the l'th harmonic is pulsed or unvoiced;
M
l
=M
l,n if the l′th harmonic is voiced (1)
where Ml,n is the enhanced spectral magnitude after noise suppression, K is the FFT size (typically K=256), and f0 is the fundamental frequency normalized to the sampling rate (8000 Hz). The final spectral magnitudes, Ml, are quantized to form quantizer values b2, b3, bL+1, where L equals the number of harmonics in the frame. Finally, FEC coding is applied to the quantizer values and the result of the coding forms the output bit stream from the enhanced MBE encoder.
The bit stream output by the enhanced MBE encoder is interoperable with the standard APCO Project 25 vocoder. The standard decoder can decode the bit stream produced by the enhanced MBE encoder and produce high quality speech. In general, the speech quality produced by the standard decoder is better when decoding an enhanced bit stream than when decoding a standard bit stream. This improvement in voice quality is due to the various aspects of the enhanced MBE encoder, such as voice activity detection, tone detection, enhanced MBE parameter estimation, and noise suppression.
Voice quality can be further improved if the enhanced bit stream is decoded by an enhanced MBE decoder. As shown in
The resulting MBE parameters are then checked against Table 1 to see if they correspond to a valid tone frame (step 720). Generally, a tone frame is identified if the fundamental frequency is approximately equal to an entry in Table 1, the voicing bands for the non-zero harmonics for that tone are voiced, all other voicing bands are unvoiced, and the spectral magnitudes for the non-zero harmonics, as specified in Table 1 for that tone, are dominant over the other spectral magnitudes. When a tone frame is identified by the decoder, all harmonics other than the specified non-zero harmonics are attenuated (20 dB attenuation is typical). This process attenuates the undesirable harmonic sidelobes that are introduced by the spectral magnitude quantizer used in the vocoder. Attenuation of the sidelobes reduces the amount of distortion and improves fidelity in the synthesized tone signal without requiring any modification to the quantizer, thereby maintaining interoperability with the standard vocoder. In the case where no tone frame is identified, sidelobe suppression is not applied to the spectral magnitudes.
As a final step in procedure 700, spectral magnitude enhancement and adaptive smoothing are performed (step 725). Referring to
Next, a test is applied to determine whether the received voicing quantizer value, b1, has a value of zero, which indicates the all unvoiced state (step 815). If so, then a second test is applied to determine whether the received value of b0 equals one of the reserved values of b0 contained in the Table 2, which indicates that the fundamental frequency contains additional information on the voicing state (step 820). If so, then a test is used to check whether state variable ValidCount is greater than or equal to zero (step 830). If so, then the decoder looks up in Table 2 the channel voicing decisions corresponding to received quantizer value b0 (step 840). This is followed by an increment of the variable ValidCount, up to a maximum value of 3 (step 835), followed by mapping of the channel decisions from the table lookup into voicing bands (step 845).
In the event that bo does not equal one of the reserved values, ValidCount is decremented to a value not less than the minimum value of −10 (step 825).
If the variable ValidCount is less than zero, the variable ValidCount is incremented up to a maximum value of 3 (step 835).
If any of the three tests (steps 815, 820, 830) is false, then the voicing bands are reconstructed from the received value of b1 as described for the standard vocoder in the APCO Project 25 Vocoder Description (step 850).
Referring again to
The unvoiced signal component synthesis (step 915) involves weighting a white noise signal and combining frames with windowed overlap-add as described for the standard vocoder. Finally, the three signal components are added together (step 920) to form a sum that constitutes the output of the enhanced MBE decoder.
Note that while is the techniques described are in the context of the APCO Project 25 communication system and the standard 7200 bps MBE vocoder used by that system, the described techniques may be readily applied to other systems and/or vocoders. For example, other existing communication systems (e.g., FAA NEXCOM, Inmarsat, and ETSI GMR) that use MBE type vocoders may also benefit from the described techniques. In addition, the described techniques may be applicable to many other speech coding systems that operate at different bit rates or frame sizes, or use a different speech model with alternative parameters (e.g., STC, MELP, MB-HTC, CELP, HVXC or others) or which use different methods for analysis, quantization and/or synthesis.
Other implementations are within the scope of the following claims.
This application is a continuation (and claims the benefit of priority under 35 U.S.C. §120) of U.S. patent application Ser. No. 10/292,460, filed Nov. 13, 2002, now allowed, which is incorporated by reference.
| Number | Date | Country | |
|---|---|---|---|
| Parent | 10292460 | Nov 2002 | US |
| Child | 13169642 | US |
