1. Field of the Invention
The present invention relates to a received voice processing apparatus. More particularly, the present invention relates to a received voice processing apparatus for clarifying received voice in a cellular phone.
2. Description of the Related Art
In recent years, cellular phones become widespread.
As the voice decoder 16, a device that efficiently compresses and decompresses a voice signal by using digital signal processing can be used. For example, a decoder of CS-ACELP (Conjugate Structure-Algebraic CELP) can be used. Or, decoder of VSELP (Vector Sum Excited Linear Prediction), ADPCM decoder, PCM decoder and the like can be used.
The cellular phone is often used in the outside. Thus, there are many cases in which received voice can not be heard well when the level of surrounding noise such as traffic noise is high. This phenomenon occurs due to a masking effect by the surrounding noise. That is, low voice can not be heard well and clearness of voice decreases due to the masking effect.
In the voice sending side, a noise canceler is implemented for removing the surrounding noise. However, as for the received voice, any effective measure is not taken. Thus, a user of the cellular phone can not hear well the voice of the party on the other end of the cellular phone under a noisy environment. Conventionally, for hearing the voice well, the user adjusts the volume of the received voice.
Some methods have been contrived for automatically adjusting the received voice according to surrounding noise, in which it is not necessary for the user to change the volume of the received voice. For example, Japanese laid-open patent application No. 9-130453 discloses a method for adjusting the volume of the received voice according to surrounding voice, in which a method on speed of increasing or decreasing the volume of the voice is disclosed.
In a method disclosed in Japanese laid-open patent application No. 8-163227, to prevent that the level of voice is erroneously measured due to voice input from the microphone, a means for discriminating between voice and non-voice is provided, so that accuracy of level measurement is increased. However, only the volume of the received voice adjusted in this method, in which frequency characteristics of voice are not considered.
In Japanese laid-open patent applications No. 5-284200 and No. 8-265075, tone of received voice is changed according to surrounding voice, and, range of voice that is reproduced is adjusted. In addition, in Japanese laid-open patent application No. 2000-349893, masking amount of voice is calculated from surrounding noise, then, a voice emphasizing process is performed.
However, there are following problems for the above-mentioned methods.
As for the Japanese laid-open patent applications No. 9-130453 and No. 8-163227 in which only automatic adjustment of the volume of the received voice is performed, it is predicted that distortion occurs when the voice is largely amplified, which causes user discomfort. In addition, clearness is not improved to a sufficient degree.
As for the Japanese laid-open patent applications No. 5-284200 and No. 8-265075 in which tone is changed and voice range is restricted, since, voice quality is changed, the user may feel something wrong. Thus, clearness is not improved to a sufficient degree.
The Japanese laid-open patent application No. 2000-349893 deals with voice recorded in a recording medium, and does not deal with real time processing. In addition, since the voice emphasizing processing is conventional band division type dynamic range compression processing, there is a problem accompanied by band division. That is, different compression presses is performed on each band of the voice signal, and the compressed voice signal is expanded and synthesized. Thus, the user may feel something wrong due to discontinuity between bands.
An object of the present invention is to provide a received voice processing apparatus for improving clearness of received voice without largely changing the volume of the voice, in which degradation and change of the voice quality are reduced to a minimum.
The object of the present invention is achieved by a received voice processing apparatus including:
a voice frequency analysis part for calculating a voice spectrum by performing frequency analysis on a received voice signal;
a target spectrum calculation part for calculating, for each frequency band, a target spectrum on the basis of a compression ratio for the voice spectrum;
a gain calculation part for calculating, for each frequency band, a gain value for amplifying the voice spectrum to the target spectrum;
a filter coefficient calculation part for calculating a filter coefficient from the gain value; and
a filer part for processing the received voice signal by using the filter coefficient.
According to the above-mentioned invention, the received voice is amplified to a level such that a part of low signal level in the received voice such as a consonant can be heard. Thus, clearness of the received voice can be improved without largely changing the volume of the voice, in which degradation and change of the voice quality are reduced to a minimum.
Other objects, features and advantages of the present invention will become more apparent from the following detailed description when read in conjunction with the accompanying drawings, in which:
In
The frequency analysis part 31 calculates magnitude of each frequency component of the received voice signal (power spectrum). In the following, the power spectrum will be simply referred to as “spectrum”. FFT (Fast Fourier Transform) is most appropriate for use as the frequency analysis part 31 from the viewpoint of calculation amount. However, other methods can be used, such as DFT (Discrete Fourier Transformation), filter bank, wavelet transform and the like. The voice spectrum output from the frequency analysis part 31 is provided to a target spectrum calculation part 33 and to a gain calculation part 34.
The target spectrum calculation part 33 calculates a target spectrum by compressing and amplifying the voice spectrum according to a fixed compression ratio supplied from an internal table 35 beforehand, and supplies the target spectrum to the gain calculation part 34.
Under a noisy environment, noise may drown out a low voice in many cases. However, when the voice is amplified according to the present invention, the lower the voice is, the signal is amplified with greater ratio. Thus, the voice that may be drown in the noise can be easily heard. The target spectrum is obtained by performing such compression and amplification for each frequency.
A different compression ratio is set for each frequency band, so that compression and amplification are performed by using different ratio for each frequency band. Generally, the level of the received voice is large in a low frequency, and the level is small in a high frequency. Thus, it is not necessary to much compress the level of the voice signal in the low frequency. On the other hand, it is necessary to largely compress the level in high frequency since the high frequency part of the voice signal may be drown out in the surrounding noise.
In the target spectrum calculation part 33, the band of the voice is divided into N parts, and a spectrum of the received voice (referred to as Spi(n)) is converted to the target spectrum (referred to as Spe(n)) for each n, wherein n=1˜N. For this conversion, a function represented by
In
The gain calculation part 34 compares the voice spectrum from the frequency analysis part 31 and the target spectrum, and calculates a gain value (difference value between the voice spectrum and the target spectrum) for each frequency band necessary for amplifying the voice spectrum into the target spectrum. Assuming that n=1˜N, and assuming that a logarithm of gain is Gdb(n),
Gdb(n)=Spe(n)−Spi(n).
Then, the gain that is represented by logarithm (dB) is converted to a linear value in consideration of designing filter coefficients later. For obtaining linear gain value Glin(n), following equation is used.
Glin(n)=pow(10, Gdb(n)/20)
In this equation, pow(a, b) means “a” to the power of “b”.
The time constant control part 36 performs a time constant control process by using a fixed time constant supplied from the internal table 35, so that the gain value from the gain calculation part 34, that is different for each frequency band, changes smoothly with respect to time. That is, by the time constant control process, it can be avoided that the change of the gain value with respect to time becomes steep.
When a gain value at the current time is smaller than a previous gain value, the gain value is decreasing. At this time, the amplitude of the voice is increasing. It means that the voice is rising. Thus, gain adjustment is performed by using the following equation.
Gain output=(gain value at the current time)×a0+(previous gain value)×a1
When the gain value at the current time is greater than the previous gain value, the gain is increasing. That is, the amplitude of the voice is decreasing. It means that the voice is falling. In this case, following equation is used for gain adjustment.
Gain output=(gain value at the current time)×b0+(previous gain value)×b1
For example, in order to steeply rise voice, the coefficient a0 is set to be large, and the coefficient a1 is set to small. On the other hand, in order to smoothly rise voice, the coefficient a0 is set to be small, and the coefficient a1 is set to be large, so that the gain value does not change largely from the previous gain value and the change of gain becomes smooth. In the case of falling of voice, the change of gain can be controlled in the same way.
For example, assuming that a rising time is X (sec) and the sampling frequency is sf, the coefficients a0 and a1 are determined by the following equations.
a0=exp(−1.0/(sf×X+1.0))
a1=1.0−a0
For example, by setting the rising time to be several micro seconds, and setting a falling time to be several tens ˜ a hundred micro second, feeling of voice deformation becomes small.
A filter designing part 37 samples the gain values of each frequency band, as sampling data on frequency axis, by using a frequency sampling method such as FFT or DFT, and performs inverse Fourier transform on the sampled data, so that a digital filter having the frequency characteristics is designed. Then, the filter designing part 37 sets filter coefficients on the filter part 32. The filter coefficients change according to time.
Or, after designing an analog filter having predetermined frequency characteristics by using designing algorithm of an analog filter, the filter designing part 37 can convert analog transfer function into digital filter coefficients by using bilinear conversion and the like.
The filter coefficients are set in the filter part 32, so that the filter part 32 performs filtering on the received voice signal supplied from the voice decoder 16. The filter part 32 generally uses the digital filter. The type of the digital filter can be either of FIR (Finite Impulse Response) or IIR (Infinite Impulse Response). Accordingly, the spectrum of the received voice signal is converted into the target spectrum and is output, so that the signal is reproduced and the reproduced voice is output from the speaker 20 via the amplifier 18.
According to this embodiment, the level of the voice signal is amplified, such that signal of a small level such as a consonant sound can be heard, so that the voice can be heard clearly.
In
The frequency analysis part 31 calculates voice spectrum that represents each frequency component of the received voice signal. FFT (Fast Fourier Transform) is most appropriate for the frequency analysis part 31 from the viewpoint of calculation amount. However, other methods can be used, such as DFT (Discrete Fourier Transformation), filter bank, wavelet transform and the like. The voice spectrum output from the frequency analysis part 31 is provided to the target spectrum calculation part 33 and to the gain calculation part 34.
A signal input from the transmission microphone 41 is analyzed by a frequency analysis part 42 as surrounding noise, so that a noise spectrum is calculated.
A compression ratio calculation part 43 obtains a compression ratio for each frequency from the noise spectrum. For this purpose, noise spectrum and corresponding compression ratio are predetermined, and compression ratio corresponding to the noise spectrum is read from the internal table 35. Accordingly, by increasing the compression ratio in a frequency band in which the noise level is large, the voice can be amplified to a level at which the voice can be heard, so that clearness can be kept.
Assuming that the noise spectrum is Spn(n), the compression ratio C(n) corresponding to Spn(n) is read from the internal table 35. Also, C(n) can be calculated by using a following equation,
C(n)=f1(Spn(n))
wherein f1 is a function for calculating the compression ratio from the noise spectrum. For example, following equations can be used as f1.
The target spectrum calculation part 33 calculates the target spectrum by compressing and amplifying the voice spectrum according to the compression ratio supplied from the compression ratio calculation part 43, and supplies the target spectrum to the gain calculation part 34.
Under a noisy environment, noise may drown out a low voice. However, when the voice is amplified according to the present invention, the voice is amplified such that the smaller the voice is, the greater the ratio of the amplification is. Thus, the voice that may be drown in the noise can be easily heard. The target spectrum is obtained by performing such compression and amplification for each frequency.
A different compression ratio is set for each frequency band, so that compression and amplification are performed by using a different ratio for each frequency band. Generally, the level of the received voice is high in a low frequency, and the level is low in a high frequency. Thus, it is not necessary to largely compress the level of the voice signal in low frequencies. On the other hand, it is necessary to largely compress the level in high frequency since the high frequency part of the voice signal may be drown out in the surrounding noise.
In the target spectrum calculation part 33, the band of the voice is divided into N parts, and received voice spectrum (referred to as Spi(n)) is converted to the target spectrum (referred to as Spe(n)) for each n, wherein N=1˜n. For this conversion, a function represented by
The gain calculation part 34 compares the voice spectrum from the frequency analysis part 31 with the target spectrum, and calculates a gain value (difference value between the voice spectrum and the target spectrum) for each frequency band necessary for amplifying the voice spectrum into the target spectrum.
The time constant control part 36 performs a time constant control process by using fixed time constants supplied from the internal table 35, so that the gain value from the gain calculation part 34, that is different for each frequency band, changes smoothly with respect to time. That is, by the time constant control process, it can be avoided that the change of the gain value with respect to time becomes steep.
When a gain value at the current time is smaller than a previous gain value, the gain is lowering. At this time, the amplitude of a waveform of the voice is increasing. It means that the voice is rising. Thus, gain adjustment is performed by using the following equation.
Gain output=(gain value at the current time)×a0+(previous gain value)×a1
When the gain value at the current time is greater than the previous gain value, the gain is increasing. That is, the amplitude of the voice waveform is decreasing. It means that the voice is falling. In this case, a following equation is used for gain adjustment.
For example, assuming that rising time is X (sec) and sampling frequency is sf, the coefficients a0 and a1 are determined by the following equations.
a0=exp(−1.0/(sf×X+1.0))
a1=1.0−a0
For example, by setting rising time to be several micro seconds, and setting falling time to be several tens ˜ a hundred micro second, feeling of voice deformation becomes small.
The filter designing part 37 samples the gain values of each frequency band as sampling data on a frequency axis by using a frequency sampling method such as FFT or DFT, and performs inverse Fourier transform on the sampled data, so that a digital filter having the frequency characteristics is designed. Then, the filter designing part 37 sets filter coefficients on the filter part 32.
The filter coefficients are set in the filter part 32, so that the filter part 32 performs filtering on the received voice signal supplied from the voice decoder 16. Accordingly, the spectrum of the received voice signal is converted into the target spectrum and is output, so that the signal is reproduced and the reproduced voice is output from the speaker 20 via the amplifier 18.
In
The frequency analysis part 31 calculates a voice spectrum that represents each frequency component of the received voice signal. FFT (Fast Fourier Transform) is most appropriate for the frequency analysis part 31 from the viewpoint of calculation amount. However, other methods can be used, such as DFT (Discrete Fourier Transformation), filter bank, wavelet transform and the like. The voice spectrum output from the frequency analysis part 31 is provided to a frequency characteristic difference calculation part 51.
A signal input from the transmission microphone 41 is analyzed by the frequency analysis part 42 as the surrounding noise, so that noise spectrum is calculated, and provided to the frequency characteristic difference calculation part 51.
The frequency characteristic difference calculation part 51 calculates the difference between the voice spectrum and the noise spectrum. Assuming that the difference is Spd(n), Spd(n) can be represented by the following equation.
Spd(n)=Spi(n)−Spn(n)
The gain calculation part 52 calculates gain values for each frequency from the difference Spd(n). The gain value corresponding to Spd(n) may be read from the internal table 35, in addition, it may be calculated. Assuming that logarithm of Spd(n) is Gdb(n), the compression ratio C(n) for each frequency can be calculated by
C(n)=f2(Gdb(n)),
wherein f2 is a function for calculating the gain value from the difference between the spectrums. For example, following equations can be used as f2.
The time constant control part 36 performs a time constant control process by using fixed time constants supplied from the internal table 35, so that the gain value from the gain calculation part 34, that is different for each frequency band, changes smoothly with respect to time. That is, by the time constant control process, it can be avoided that the change of the gain value with respect to time becomes steep.
A filter designing part 37 samples the gain values of each frequency band as sampling data on frequency axis by using a frequency sampling method such as FFT or DFT, and performs inverse Fourier transform on the sampled data, so that a digital filter having the frequency characteristics is designed. Then, the filter designing part 37 sets filter coefficients on the filter part 32.
The filter coefficients are set in the filter part 32, so that the filter part 32 performs filtering on the received voice signal supplied from the voice decoder 16. Accordingly, the spectrum of the received voice signal is converted into the target spectrum and is output, so that the signal is reproduced and the reproduced voice is output from the speaker 20 via the amplifier 18.
According to this embodiment, adaptive processing becomes possible for each frequency, such that, for example, when noise is much larger than the received voice, the gain is further increased. On the other hand, when the received voice is enough larger than the noise, the amplification is not performed.
In
The frequency analysis part 31 calculates a voice spectrum that represents each frequency component of the received voice signal. FFT (Fast Fourier Transform) is most appropriate for the frequency analysis part 31 from the viewpoint of calculation amount. However, other methods can be used, such as DFT (Discrete Fourier Transformation), filter bank, wavelet transform and the like. The voice spectrum output from the frequency analysis part 31 is provided to the target spectrum calculation part 33, the gain calculation part 34 and the masking amount calculation part 61.
A signal input from the transmission microphone 41 is analyzed by the frequency analysis part 42 as the surrounding noise, so that noise spectrum is calculated, and provided to the masking amount calculation part 61.
The masking amount calculation part 61 calculates masking amount for each frequency from the noise spectrum and the voice spectrum. Generally, in the masking, a signal having a large level masks a signal having a small level. Therefore, difference between magnitudes of the noise spectrum and the voice spectrum is calculated first. Then, only when the difference is greater than a predetermined value, masking calculation is performed.
First, a calculation method of frequency masking will be described by using
Spd(n)=Spn(n)−Spi(n)
Only when Spd(n)>Thref, frequency masking calculation is performed. Thref is a threshold value and is a constant.
It is known that the closer the frequency of the masked signal is to the frequency of the masking signal, the stronger the masking effect is, and the masking effect becomes weak as the frequencies are apart. Thus, by using the following function, masking amount Mask (n) (dB) applied to the received voice by the noise signal is calculated. Assuming that frequency that is masked by the noise signal is n′,
Mask(n′)=Spd(n)−C1×(n′−n), when n′≧n, and
Mask(n′)=Spd(n)−C2×(n−n′), when n′<n, wherein C1 and C2 are positive constant coefficients.
Next, masking of time axis is considered. A calculation method of time masking will be described with reference to
Difference Spd (t, n) between the voice spectrum and the noise spectrum at a frequency band n at a time t is represented by the following equation.
Spd(t, n)=Spn(t, n)−Spi(t, n)
Then, only when Spd(t, n)>Thret, time masking is calculated. Thret is a threshold and a constant.
Assuming that masking amount in which a signal of time t′ is masked by a signal of time t at a frequency n is Mask (t′, n),
Mask(t′, n)=Spd(t, n)−C3×(t′−t)
wherein C3 is a positive constant coefficient and the time t′ is a later time than the time t. That is, (t′−t)>0.
The masking amount may be calculated for both of frequency masking and time masking. Also, the masking amount may be calculated either of those.
A compression ratio calculation part 62 obtains compression ratio for each frequency from the masking amount. For this purpose, masking amount and corresponding compression ratio are predetermined, and compression ratio corresponding to the masking amount is read from the internal table 35. Accordingly, by increasing the compression ratio in a frequency band in which masking amount is large, the voice can be amplified to a level at which the voice can be heard, so that clearness can be kept.
The target spectrum calculation part 33 calculates the target spectrum by compressing and amplifying the voice spectrum according to the compression ratio supplied from the compression ratio calculation part 62, and supplies the target spectrum to the gain calculation part 34.
The gain calculation part 34 compares the voice spectrum from the frequency analysis part 31 and the target spectrum, and calculates a gain value (difference value between the voice spectrum and the target spectrum) for each frequency band necessary for amplifying the voice spectrum into the target spectrum.
The time constant control part 36 performs a time constant control process by using fixed time constants supplied from the internal table 35, so that the gain value from the gain calculation part 34, that is different for each frequency band, changes smoothly with respect to time. That is, by the time constant control process, it can be avoided that the change of the gain value with respect to time becomes steep.
A filter designing part 37 samples the gain values of each frequency band as sampling data on frequency axis by using a frequency sampling method such as FFT or DFT, and performs inverse Fourier transform on the sampling data, so that a digital filter having the frequency characteristics is designed. Then, the filter designing part 37 sets filter coefficients on the filter part 32.
The filter coefficients are set in the filter part 32, so that the filter part 32 performs filtering on the received voice signal supplied from the voice decoder 16. Accordingly, the spectrum of the received voice signal is converted into the target spectrum and is output, so that the signal is reproduced and the reproduced voice is output from the speaker 20 via the amplifier 18.
In
The frequency analysis part 31 calculates the voice spectrum that represents each frequency component of the received voice signal. FFT (Fast Fourier Transform) is most appropriate for the frequency analysis part 31 from the viewpoint of calculation amount. However, other methods can be used, such as DFT (Discrete Fourier Transformation), filter bank, wavelet transform and the like. The voice spectrum output from the frequency analysis part 31 is provided to the target spectrum calculation part 33, the gain calculation part 34 and the masking amount calculation part 61.
A signal input from the transmission microphone 41 is analyzed by the frequency analysis part 42 as the surrounding noise, so that noise spectrum is calculated, and provided to the masking amount calculation part 61.
The masking amount calculation part 61 calculates masking amount for both of the frequency masking and the time masking from the noise spectrum and the voice spectrum. The gain calculation part 71 reads calculated masking amount for each frequency, and reads a gain value corresponding to the masking amount from the internal table 35. In this case, the larger the masking amount is, the larger the gain is.
The time constant control part 36 performs a time constant control process by using fixed time constants supplied from the internal table 35, so that the gain value from the gain calculation part 34, that is different for each frequency band, changes smoothly with respect to time. That is, by the time constant control process, it can be avoided that the change of the gain value with respect to time becomes steep.
A filter designing part 37 samples the gain values of each frequency band as sampling data on frequency axis by using a frequency sampling method such as FFT or DFT, and performs inverse Fourier transform on the sampling data, so that a digital filter having the frequency characteristics is designed. Then, the filter designing part 37 sets filter coefficients on the filter part 32.
The filter coefficients are set in the filter part 32, so that the filter part 32 performs filtering on the received voice signal supplied from the voice decoder 16. Accordingly, the spectrum of the received voice signal is converted into the target spectrum and is output, so that the signal is reproduced and the reproduced voice is output from the speaker 20 via the amplifier 18.
In
When the voice/non-voice determining part 72 determines that the input is voice, there is a high possibility that the voice is the user's voice. Thus, if the input of the transmission microphone 41 is determined to be surrounding noise, the received voice is extremely amplified. Thus, to avoid this phenomenon, a filter coefficient adjusting part 73 performs following processes.
(1) The filter coefficient adjusting part 73 replaces the filter coefficients supplied from the filter designing part 37 with an initial value (for example, a value by which amplification is not performed), and sets the initial value in the filter part 32.
(2) The filter coefficient adjusting part 73 determines the maximum value of a filter coefficient. When a filter coefficient supplied from the filter designing part 37 exceeds the maximum value, the filter coefficient is replaced by the maximum value and the maximum value is set in the filter part 32.
(3) The filter coefficient adjusting part 73 stops updating the filter coefficients of the filter part 32. That is, the filter coefficients just before the non-voice state is changed to the voice state are kept.
In each configuration shown in
As mentioned above, according to the present invention, a received voice processing apparatus is provided. The received voice processing apparatus includes: a voice frequency analysis part for calculating a voice spectrum by performing frequency analysis on a received voice signal; a target spectrum calculation part for calculating, for each frequency band, a target spectrum on the basis of a compression ratio for the voice spectrum; a gain calculation part for calculating, for each frequency band, a gain value for amplifying the voice spectrum to the target spectrum; a filter coefficient calculation part for calculating a filter coefficient from the gain value; and a filer part for processing the received voice signal by using the filter coefficient.
According to the above-mentioned invention, the received voice is amplified to a level such that a part of low signal level in the received voice such as a consonant can be heard. Thus, clearness of the received voice can be improved without largely changing the volume of the voice, in which degradation and change of the voice quality are reduced to a minimum.
The received voice processing apparatus may further includes: a surrounding noise frequency analysis part for calculating a noise spectrum by performing frequency analysis on an input signal from a transmission microphone; and a compression ratio calculation part for calculating the compression ratio for each frequency band according to the noise spectrum.
Accordingly, the compression ratio can be increased in a frequency band having a high level noise. Thus, clearness of the received voice can be improved without largely changing the volume of the voice, in which degradation and change of the voice quality are reduced to a minimum.
The received voice processing apparatus may includes: a voice frequency analysis part for calculating a voice spectrum by performing frequency analysis on a received voice signal; a surrounding noise frequency analysis part for calculating a noise spectrum by performing frequency analysis on an input signal from a transmission microphone; a gain calculation part for calculating, for each frequency band, a gain value for amplifying the voice spectrum according to a difference between the voice spectrum and the noise spectrum; a filter coefficient calculation part for calculating a filter coefficient from the gain value; and a filer part for processing the received voice signal by using the filter coefficient.
Accordingly, adaptive processing becomes possible, such that, for example, when noise is much larger than the received voice, the gain is further increased. On the other hand, when the received voice is enough larger than the noise, the amplification is not performed.
Also, the received voice processing apparatus may include: a voice frequency analysis part for calculating a voice spectrum by performing frequency analysis on a received voice signal; a surrounding noise frequency analysis part for calculating a noise spectrum by performing frequency analysis on an input signal from a transmission microphone; a masking amount calculation part for calculating masking amount by using the noise spectrum and the voice spectrum; a gain calculation part for calculating, for each frequency band, a gain value for amplifying the voice spectrum according to the masking amount; a filter coefficient calculation part for calculating a filter coefficient from the gain value; and a filer part for processing the received voice signal by using the filter coefficient.
The received voice processing apparatus may further includes: a compression ratio calculation part for calculating a compression ratio for each frequency band according to the masking amount; a target spectrum calculation part for calculating, for each frequency band, a target spectrum on the basis of the compression ratio; wherein the gain calculation part calculates the gain value by using the voice spectrum and the target spectrum instead of the masking amount.
Accordingly, the compression ratio can be increased in a frequency band having large masking amount, so that the voice can be properly amplified.
The received voice processing apparatus may further include: a time constant control part for performing time constant control on the gain value, and supplying the gain value on which the time constant control is performed to the filter coefficient calculation part.
Accordingly, it can be avoided that the change of the gain value with respect to time becomes steep, so that the gain value change smoothly.
The received voice processing apparatus may includes: a voice/non-voice determining part for determining whether an input signal from a transmission microphone is voice of the user of the received voice processing apparatus or not; and a filter coefficient adjusting part for supplying the filter coefficient to the filter part when the input signal is not the voice of the user.
Accordingly, the voice is not extremely amplified while the user is speaking.
The received voice processing apparatus may includes: a compensation filter for compensating for a diffraction effect due to the head of the user of the received voice processing apparatus for the input signal, and supplying the input signal to the surrounding noise frequency analysis part.
Accordingly, frequency characteristics of noise that is actually heard from the ear can be estimated, so that the process becomes in touch with reality, and clear received voice can be obtained.
The present invention is not limited to the specifically disclosed embodiments, and variations and modifications may be made without departing from the scope of the present invention.
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