JP3141853B2 - Audio signal processing method - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an audio signal processing method for restoring an audio signal from a compression-coded signal obtained by compressing an audio signal by performing so-called block floating processing.

[0002]

2. Description of the Related Art Conventionally, as an audio signal processing method, a high-efficiency encoding technique for compressing and encoding an audio signal includes, for example, an input audio signal (digital audio data) at a predetermined time (a predetermined time frame). )
There is a method in which data is divided into a plurality of blocks on the frequency axis, so-called block floating processing is performed for each block, and data for each block is quantized by adaptive bit allocation.

Here, the above-mentioned block floating processing is basically to increase the precision at the time of quantization by multiplying each word in the block by a common value to increase the precision.
Specifically, for example, the largest (maximum absolute value) among the absolute values of the words in the block is searched for, and a floating coefficient common to all the words in the block such that the maximum absolute value is not saturated. Is an example in which a floating process is performed by using a. As a simpler method, there is a 6 dB unit floating using a bit shift.

On the encoder side of a system to which the audio signal processing method for performing the block floating processing is applied, usually, for example, a value of a scale factor SF as a floating coefficient, The data of the word length WL indicating the difference between the scale factor SF and the allowable noise level obtained for each block in consideration of the so-called masking effect is referred to as quantized block data (hereinafter referred to as main information, for example). ) Together with recording or transmission on a medium.

[0005] The masking effect refers to a phenomenon in which a certain sound is masked by another sound and becomes inaudible due to human auditory characteristics. In other words, the masking is a phenomenon in which a certain signal masks another signal and makes it inaudible. This masking effect includes a time-axis masking effect by an audio signal on a time axis and a masking effect on a frequency axis. And the same time masking effect by the signal of Due to these masking effects, even if noise is present in the masked portion, this noise will not be heard. For this reason, in an actual audio signal, the noise within the masked range is regarded as acceptable noise.

[0006]

On the encoder side of a system to which the conventional audio signal processing method is applied, the number of the recorded or transmitted parameters BF is fixed for all time frames. Things are common. FIG. 9 shows how data in each time frame is recorded (or transmitted) on the encoder side of the conventional audio signal processing system. In the case of the example of FIG. 9, it is necessary to record or transmit the value of the parameter BF even for a block to which bits are not actually allocated. Therefore, the number of bits allocated to the audio data is correspondingly large. In particular, when the compression rate is high (when the bit rate is low), it is difficult to ensure sufficient sound quality on the decoder side (decoder side of the audio signal processing system) corresponding to the encoder side. .

On the other hand, as shown in FIG. 10, for a block to which bits are not actually allocated (a block having a word length WL = 0), the value of the scale factor SF is not recorded or transmitted, and A system that allocates as many bits to audio data as possible has also been proposed.
In FIG. 10, the number of scale factors SF recorded by four blocks to which no bits are allocated (blocks to which 0 bits are allocated) is reduced.

However, in this case, the data having the word length WL needs to be always recorded or transmitted. When the scale factor SF is read on the subsequent decoder side, it is necessary to check for each block whether the word length WL of the block is not 0.

Further, on the encoder side, usually, after calculating the number of bits necessary for quantizing the audio signal of each block by a calculation or the like for obtaining the masking, the total number of bits allocated to the time frame is calculated. And the adjustment of the bit allocation to each block is performed. However, at this time, if the scale factor SF of the block changes depending on the bit allocation of each block as described above, the total number of bits that can be allocated to the main information (audio data) also changes accordingly. Then, the adjustment of the bit allocation becomes very complicated. Therefore, there is a possibility that the decoding process becomes complicated on the subsequent decoder side.

Therefore, the present invention has been proposed in view of the above-mentioned situation, and even if the bit allocation is adjusted relatively easily on the encoder side and the bit allocation is adjusted, the decoding It is an object of the present invention to provide an audio signal processing method capable of restoring a good audio signal without deteriorating sound quality.

[0011]

SUMMARY OF THE INVENTION An audio signal processing method according to the present invention has been proposed in order to achieve the above-mentioned object, and each audio signal is divided into a plurality of audio signals on a frequency axis at predetermined time frames. Up to a quantized signal obtained by performing block floating processing for each block and quantizing data of each block by adaptive bit allocation, and a band required for each time frame among parameters of each block related to the block floating processing. Is an audio signal processing method for restoring an audio signal from a coded signal having the parameters of the above-described parameters and the number of parameters of each time frame. The number of parameters is extracted from the coded signal. The parameters are extracted from the encoded signal according to the information, and the encoded signal is Removed quantized signals from, in which so as to restore the audio signal from the quantized signal based on the number information and the parameter in the parameter.

That is, as a band of an audio signal that is actually recorded or transmitted for each time frame, for example, high-range recording or transmission is not performed because it is hardly perceived (it is hard to hear). In such a case, the parameters (scale factor, word length value) of the high-frequency block in the time frame are not recorded or transmitted, and the bits corresponding to the high-frequency frequency information are stored in the low-frequency main information (block (Quantized data). In this case, the data to be recorded or transmitted is the low frequency parameters and the main information.
In this case, the number of parameters recorded or transmitted for each time frame is also recorded (or transmitted). Therefore, if the main information is decoded based on the number and parameters of these parameters, an audio signal with good sound quality can be reproduced.

As described above, according to the audio signal processing method of the present invention, the coded signal, together with the quantized signal, of the parameters for each block, up to the band required for each time frame, Therefore, if a quantization process is performed on the quantized signal using the parameters included in the encoded signal and the number of parameters included in the encoded signal, a high-quality audio signal can be restored.

[0014]

Embodiments of the present invention will be described below with reference to the drawings.

In the audio signal processing method on the encoder side of the present embodiment, the time-series input audio signal TS is converted into a frequency-axis signal (spectral signal SP) for each predetermined time frame, and this spectral signal SP is converted. Divide into blocks for each band, perform block floating processing for each block, quantize data for each block by adaptive bit allocation, record or transmit the quantized audio signal, and An audio signal processing method for recording or transmitting a floating coefficient (scale factor SF) as a parameter BF related to processing and data of a word length WL, as shown in FIG. The parameter BF for each block is With the parameters BF is recorded or transmitted to a band required, the number N of parameters BF being the recording or transmission is also obtained by such recording or transmission.

FIG. 1 shows one time frame and the one time frame.
3 shows blocks of a plurality of frequency bands in one time frame. The main information in FIG. 1 is data of a quantized block (data of each quantized block of the spectrum signal SP for each block obtained by frequency-dividing the audio signal TS).

Here, the effectiveness of the present embodiment is based on the following facts.

That is, in a high band of, for example, 10 kHz or more, a so-called minimum audibility limit, which will be described later, based on human auditory characteristics is high. In addition, since the masking effect by a low-frequency signal works effectively, quantization noise is less than that. Sound quality degradation is less likely to be perceived by the ears even if the band becomes larger than the band (for example, 10 kHz or less). In particular, even if a signal component particularly in a band of 15 kHz or less is deleted (0 bit allocation), in many cases, the difference is not recognized in terms of audibility.

Therefore, in this embodiment, as shown in FIG. 1, the scale factor SF as a floating coefficient, which is a parameter BF for each block related to the block floating process, and the number of bits allocated in quantization. The data of the word length WL corresponding to the above is recorded or transmitted up to the band that requires the parameter BF (that is, only the low frequency band that is important for hearing) for each time frame. As a result, many bits can be allocated to low-frequency codes that cannot be omitted because they are important for hearing, and as a result, the sound quality is improved.

At the minimum audible limit, if the absolute noise level is lower than the minimum audible limit, the noise will not be heard. This minimum audibility may vary with the same coding, for example, due to differences in playback volume during playback, but in a realistic digital system, the way in which music enters the 16-bit dynamic range, for example, varies significantly. For example, if quantization noise in the most audible frequency band around 4 kHz is not heard, it is considered that quantization noise below the level of the minimum audible curve is not heard in other frequency bands.

Further, in the above-described manner, the number of the parameter BF changes for each time frame. Therefore, in this embodiment, the number N of the parameter BF is recorded or transmitted for each time frame. ing. In this case, the data amount of the number N is small. For example, if the number of parameters BF is about several tens, 7 bits is sufficient, and if there are two types of bands that can be taken in the time frame (two blocks), one bit is sufficient for recording or transmission. You can do it.

As described above, in the present embodiment,
As a band of an audio signal that is actually recorded or transmitted for each time frame, for example, when recording or transmission of a high frequency band is not performed because it is actually hard to perceive (it is hard to hear), Bits corresponding to the parameters BF (the value of the scale factor SF and the word length WL) of the low-frequency block are allocated for low-frequency main information, and the low-frequency parameter BF and the main information are recorded on, for example, a medium ( Or transmit). Further, the number N of parameters BF for each time frame is also recorded (or transmitted).

When the signal level in the low frequency range is low and the signal level in the high frequency range is very high, if the high frequency range signal is deleted, a difference in sound quality deterioration due to the deletion is easily perceived. . Therefore, in this case, as shown in FIG. 2, the encoding is performed such that the high-frequency signal and the parameter BF are also recorded.

FIG. 3 shows a specific configuration of an audio signal processing system to which the audio signal processing method on the encoder side according to the present embodiment is applied.

FIG. 3 shows a spectrum signal (spectral data) obtained by dividing a time-series audio signal (waveform data) by a band division filter and converting it into a signal on the frequency axis by a modified discrete cosine transform (MDCT) process. 2 shows a configuration for encoding data) SP.

That is, the encoder shown in FIG.
The time-series input audio signal (digital audio data) TS supplied to the input terminal 1 is divided into a plurality of blocks on the frequency axis for each predetermined time frame, and a block floating process is performed for each block. The data for each block is encoded by adaptive bit allocation, and parameters for each block related to the block floating processing (the scale factor SF as a floating coefficient and the word length corresponding to the bit allocation number) WL) BF is recorded for each time frame up to the band where the parameter BF is required, and a data recording circuit 19 for recording the number of parameters to be recorded is also provided. The data recorded in the data recording circuit 19 is transmitted to a configuration on the decoder side to which the audio signal processing method of the present invention described later is applied.

Here, in the encoder of this specific example, when the band of each block obtained by dividing the time frame into a plurality of frequency bands is made variable and the band is narrow, Thus, the number of parameters BF to be recorded in the data recording circuit 19 is reduced, and the reduced bits are used for main information (quantization of a spectrum signal SP for each block obtained by frequency-dividing an audio signal TS, which will be described later). At the time).

In order to do this, the time-series audio signal (waveform data) TS supplied to the input terminal 1 for each of the predetermined time frames is converted into a time for converting the supplied time-series signal into a spectrum signal. / Frequency conversion circuit 11
Is converted into a spectrum signal SP. The time / frequency conversion circuit 11 divides the time-series audio signal TS into predetermined time frames and divides each time frame into a plurality of frequency bands as shown in FIGS. Blocking is performed, and the bandwidth of each block is made variable.

When the band of the block is made variable by the time / frequency conversion circuit 11, each block obtained by dividing a time frame by, for example, band division in consideration of human auditory characteristics is used as the block width. Is made to be a block. In other words, the spectrum signal SP is divided into a plurality of bands with a bandwidth which is generally called a critical band (critical band) such that the bandwidth becomes wider as the frequency becomes higher. It is divided into three bands, namely, band, middle band and low band. Note that this critical band is a frequency band divided in consideration of human auditory characteristics, and the narrow band noise of the same strength near the frequency of a certain pure tone causes the noise of the pure tone to be masked. It is the band that you have. Further, as the division in the critical band, for example, 0 to 20 k
It is also possible to divide the whole frequency band of Hz into, for example, 25 critical bands.

The spectrum signal SP from the time / frequency conversion circuit 11 is sent to a spectrum signal quantization circuit 15 and quantized. That is, the spectrum signal quantization circuit 15 normalizes (normalizes) the supplied spectrum signal SP of each block by block floating processing, and then quantizes the spectrum signal SP with an adaptively allocated number of bits in consideration of a so-called masking effect.

Here, the above-mentioned spectrum signal quantization circuit 1
The floating factor (scale factor SF) for performing the block floating processing at 5 is supplied from the scale factor calculation circuit 13. That is, the spectrum signal SP is supplied to the scale factor calculation circuit 13. From the scale factor calculation circuit 13, for example, a peak or an average value of the spectrum signal SP for each block in a plurality of frequency bands for each time frame. Is multiplied by a predetermined coefficient to output a floating coefficient (scale factor SF).

The above-mentioned spectrum signal quantization circuit 15
In order to perform the adaptive quantization of the number of allocated bits, the spectrum signal SP is
Has also been sent to. In the masking calculation circuit 17,
As described later, the masking information MSKI of each block according to the human auditory characteristics and / or the masking information MSKI of the block of interest due to a masking effect from another block adjacent to the block of interest is obtained. The masking information MSKI from the masking calculation circuit 17 is sent to the bit allocation calculation circuit 14, and the bit allocation calculation circuit 14 uses the word length as bit allocation information for each block based on the masking information MSKI. WL data is required. That is, based on the information on the word length WL, the spectrum signal quantization circuit 15 performs adaptive quantization of the supplied spectrum signal SP for each block.

Here, the processing in the masking calculation circuit 17 and the bit allocation calculation circuit 14 is specifically performed as follows.

That is, the energy of the spectrum signal SP sent to the masking calculation circuit 17 is first calculated for each block. In calculating the energy for each block, for example, the energy for each critical band (critical band) is obtained by, for example, calculating the sum of the amplitude values within the band. Instead of the energy for each band, a peak value or an average value of the amplitude value may be used. The spectrum of the sum value of each band obtained by this energy calculation is generally called a bark spectrum.

Next, in the masking calculation circuit 17,
In order to consider the influence of the Bark spectrum on so-called masking, a convolution process in which the Bark spectrum is multiplied by a predetermined weighting function and added is performed. As a configuration for performing the convolution process, for example, a plurality of delay elements for sequentially delaying input data and a plurality of multipliers (for example, corresponding to each band) for multiplying an output from these delay elements by a filter coefficient (weighting function) 25 multipliers) and a sum adder for summing the outputs of the multipliers.

After the convolution processing is performed, a masking threshold is obtained by performing an inverse convolution processing. That is, this masking threshold becomes an acceptable noise spectrum. Here, the level at which the bark spectrum is masked by the masking threshold is obtained by subtracting the masking threshold from the bark spectrum. This masking level is sent to the bit allocation calculation circuit 14 as the masking information MSKI.

When the masking information MSKI is obtained, for example, the data indicating the minimum audibility, which is a human auditory characteristic, and the masking threshold can be synthesized. At this minimum audibility,
As described above, if the absolute noise level is below this minimum audible limit, the noise will not be heard.

The bit allocation calculation circuit 14 has, for example, a ROM in which information on the number of bits to be allocated is stored in advance.
Are provided, and information on the number of bits assigned to each band is obtained from the ROM or the like according to the level of the difference between the masking level of the masking information MSKI and the energy of each band. Further, based on the information on the number of allocated bits for each band, word length WL data corresponding to the number of allocated bits for each of the broad band, the middle band, and the low band is obtained.

The bit allocation calculating circuit 14 can correct the allowable noise level based on the masking information MSKI based on, for example, information on the equal loudness curve. Here, the equal loudness curve is a characteristic curve relating to human auditory characteristics,
For example, the sound pressure of sound at each frequency that sounds as loud as a 1 kHz pure tone is obtained and connected by a curve, and is also called a loudness iso-sensitivity curve. Note that this equal loudness curve draws substantially the same curve as the minimum audible curve. Therefore, in this equal loudness curve, for example, at around 4 kHz, even if the sound pressure falls by 8 to 10 dB from 1 kHz, it sounds as large as 1 kHz. Otherwise it doesn't sound the same size. For this reason, it can be seen that noise exceeding the level of the minimum audible curve (allowable noise level) preferably has a frequency characteristic given by a curve corresponding to the equal loudness curve. From this, it can be seen that correcting the allowable noise level in consideration of the equal loudness curve is suitable for human auditory characteristics.

Further, in the masking calculation circuit 17,
By performing the masking calculation, when the spectrum signal SP is quantized by the spectrum signal quantization circuit 15 and recorded in the data recording circuit 19, the highest band HB to be recorded is also determined. I have. In other words, by taking into account the masking effect and the like, a band that has little effect on the audible sound quality without recording is divided into a band that has an adverse effect on the audibility if not recorded. , The number of parameters BF for the block floating process is sent to the parameter number calculation circuit 18.

In the parameter number calculating circuit 18, based on the information of the band HB to be recorded, of the parameters BF of each block, a band equal to or smaller than the band HB to be recorded (ie, a low band to be recorded). The number N of parameters BF is calculated. Alternatively, the number of parameters BF in a band equal to or higher than the band HB to be recorded (that is, for example, a high band which does not need to be recorded) may be calculated.

The information of the number N is sent to the bit allocation calculation circuit 14. Therefore, the bit allocation calculation circuit 14 calculates the number of allocated bits as described above, and performs a band equal to or larger than the band HB to be recorded based on the information of the number N (that is, for example, it is not necessary to record the band). No bits are allocated to the (area). That is, in the bit allocation calculation circuit 14, if the number N is obtained,
Since the total number of bits that can be allocated to the main information is obtained, the surplus bits are allocated to the spectrum signal SP of the band up to the highest band HB to be recorded according to the number N.

The data recording circuit 19 records the number N of the parameters BF, the N parameters BF, and the quantized spectrum signal QSP.

The coded data CDT from the decoder recording circuit 19 is output via the output terminal 2.

FIG. 4 shows a specific configuration of the time / frequency conversion circuit 11 of the encoder shown in FIG.

In the configuration of FIG. 4, for example, a signal is compressed by combining a band division filter such as a QMF filter and a modified discrete cosine transform (MDCT). The above-mentioned QMF filter is a 1976R.E Crochiere, Digit
al coding of speech in subbands, Bell Syst. Tech.
J. Vol. 55, No. 8 1976. Also, ICAS
SP 83, BOSTON, PolyphaseQuadrature filter -A new s
The ubband coding technique, Joseph H. Rothweiler, describes an equal bandwidth filter partitioning technique.
Regarding the MDCT, ICASSP 1987 Subband /
Transform Coding Using Filter Bank DesignsBased on
Time Domain Aliasing Cancellation, JPPrincen,
ABBradley, Univ. Of Surrey Royal Melbourne Inst.
of Tech. Note that, instead of the MDCT, for example, the time axis can be converted to the frequency axis by performing a fast Fourier transform (FFT), a discrete cosine transform (DCT), or the like.

In the configuration shown in FIG. 4, the input audio signal TS such as a time-series PCM signal, as described above, is designed such that the higher the band, the wider the bandwidth based on the so-called critical band in consideration of human auditory characteristics. Frequency division. In this specific example, the band is roughly divided into three bands of a high band, a middle band, and a low band in consideration of the critical band. In addition, the band division may be performed in units of a critical band or a block obtained by further subdividing a critical band (critical band) width in a high band.

That is, in FIG. 4, the input audio signal TS which is an audio PCM signal of, for example, 0 to 20 kHz is supplied to the input terminal 1. The input audio signal TS is, for example, 0 to 10 kHz by a band division filter 71 such as a so-called QMF filter.
Band and a 10 kHz to 20 kHz band (high band), and signals in the 0 to 10 kHz band are also called QMF
For example, 0 to 5 by a band division filter 72 such as a filter.
It is divided into a kHz band (low band) and a 5 kHz to 10 kHz band (middle band). The high band (10 kHz to 20 kHz band) signal from the band division filter 71 is sent to an MDCT circuit 73 which is an example of an orthogonal transformation circuit, and the middle band (5 kHz to 10 kHz band) signal from the band division filter 72 is MDCT. The signal is sent to the circuit 74,
The signals in the low band (0 to 5 kHz band) from 2 are sent to the MDCT circuit 75 to be subjected to MDCT processing. These high-frequency signals subjected to the MDCT processing are supplied to a terminal 76.
The signal in the middle band is output via a terminal 77, and the signal in the low band is output via a terminal 78.

Here, each of the MDCT circuits 13, 14, 15
Specifically, the block size of (1) is such that the frequency band is widened toward the high frequency side and the time resolution is increased (the block length is shortened). That is, the number of samples in one block is set to, for example, 256 samples for the low-frequency band signal of 0 to 5 kHz and the middle-frequency signal of 5 kHz to 10 kHz band, and for the high-frequency signal of 10 kHz to 20 kHz band. , One block is divided into blocks each having a half length of each of the low-frequency and middle-frequency blocks. In this way, the number of orthogonal transform block samples in each band is the same. In addition, each band can be further adaptively divided into 、, １ ／, and the like, assuming that the temporal change of the signal is large.

FIG. 5 corresponds to the encoder of FIG.
1 shows a configuration of a decoder according to an embodiment to which an audio signal processing method of the present invention is applied.

That is, in FIG.
1 is supplied with the coded data CDT. The data CDT includes a quantized spectrum signal reading circuit 54 for reading (extracting) a quantized spectrum signal from the data CDT, a parameter number reading circuit 52 for reading (extracting) the number N of block floating parameters BF, and The data of the block floating parameter BF is read (taken out) and sent to the parameter reading circuit 53.
The quantized spectrum signal QSP from the quantized spectrum signal reading circuit 54, the data of the number N from the parameter number reading circuit 52, the parameter BF from the parameter reading circuit 53, that is, the scale factor SF and the word length WL Are sent to the spectrum signal restoration circuit 55. The spectrum signal restoration circuit 55 performs a decoding process using the supplied signal. The spectrum signal RSP decoded by the spectrum signal restoration circuit 55 is
6 is a time-series audio signal RTS, and the output terminal 5
7 is output.

FIG. 6 shows a specific configuration of the frequency / time conversion circuit 56 having the configuration of the decoder shown in FIG.

In FIG. 6, the spectrum signal RSP of each block is applied to terminals 61, 62 and 63, and signals on the frequency axis are converted by the IMDCT (inverse MDCT) circuits 64, 65 and 66 on the time axis. Converted to a signal. The signals on the time axis of these partial bands are IQMF (inverse QM
F) The signals are decoded into full-band signals by the circuits 67 and 78, and are extracted from the terminal 69.

FIG. 7 shows a flowchart of signal processing in the encoder of FIG.

That is, in the flowchart of FIG. 7, in step S1, the time / frequency conversion circuit 1
1 converts the time-series audio signal TS input into a spectrum signal (spectral data) SP. After step S1, the process proceeds to step S2, where the scale factor calculation circuit 13 performs calculation for obtaining the scale factor SF. In step S3, the masking calculation processing is performed by the masking calculation circuit 17, and in step S4, the band to which bits are to be allocated and the number N of parameters BF for the block floating processing are determined by the parameter number calculation circuit 18. In step S5, the bit allocation calculation circuit 14 calculates the bit allocation and the word length WL.
Is calculated. In step S6, the spectrum signal quantization circuit 15 performs quantization processing of the spectrum signal (spectral data). Finally, step S7
Then, the data recording circuit 19 records the number N of data, records the parameter BF of the block floating process (records the parameter BF of the band to be recorded), and records the quantized spectrum signal (spectral data). Recording of QSP data is performed.

FIG. 8 shows a flowchart of the processing in the configuration of the decoder shown in FIG.

That is, in the flowchart of FIG. 8, first, in step S11, the number N of parameters BF is first read out by the parameter number reading circuit 52, and then in step S12, the number N of parameters is read out by the parameter reading circuit 53. Only parameter B
Read the data of F. Subsequently, in step S13, the quantized spectrum signal reading circuit 54 reads the quantized spectrum signal QSP according to the word length WL of the parameter BF. In step S14, the scale factor SF and the word length WL are output from the spectrum signal restoring circuit 55.
, The read out quantized spectrum signal QSP is restored as an approximate value of the original spectrum signal SP (the spectrum signal RSP). Finally, in step S15, the frequency / time conversion circuit 57
The audio signal RTS is restored by subjecting the spectrum signal RSP to processing opposite to that of the MDCT (inverse MDCT) and passing through a band synthesis filter.

In the above-described embodiment, a system for encoding a signal obtained by converting a time-series input audio signal TS into a spectrum signal SP has been described. The present invention can also be applied to a system in which coding is performed by dividing into bands (so-called sub-band coding).

[0059]

As described above, in the audio signal processing method according to the present invention, the parameter number information is extracted from the encoded signal, the parameter is extracted from the encoded signal according to the parameter number information, and the parameter is extracted based on the parameter. By extracting the quantized signal from the encoded signal and restoring the audio signal from the quantized signal based on the number information and the parameters of the parameters, the bit allocation can be relatively easily adjusted on the encoder side. Even if the adjustment is made, a good audio signal can be restored without deteriorating the sound quality on the decoding side.

That is, for the reason that it is difficult to actually perceive, for example, when a high-frequency signal is not recorded, even if bits for the high-frequency parameter are allocated for quantization of a low-frequency audio signal, decoding is not possible. Sometimes a good audio signal can be reproduced. Further, even when a high level signal includes a high level, a good audio signal can be reproduced at the time of decoding without narrowing the band. Further, these processes can be realized by a simple configuration.

[Brief description of the drawings]

FIG. 1 is a diagram for describing an example of data recording or transmission (when a high-frequency signal is not recorded or transmitted) in the present embodiment.

FIG. 2 is a diagram for describing an example of data recording or transmission (when a high-frequency signal is also recorded or transmitted) in the present embodiment.

FIG. 3 is a block circuit diagram illustrating a schematic configuration of an encoder.

FIG. 4 is a block circuit diagram showing a specific configuration of a time / frequency conversion circuit having the configuration of FIG. 3;

FIG. 5 is a block circuit diagram showing a schematic configuration of a configuration on a decoder side according to an embodiment to which the audio signal processing method of the present invention is applied;

FIG. 6 is a block circuit diagram showing a specific configuration of a frequency / time conversion circuit having the configuration of FIG. 5;

FIG. 7 is a flowchart of a process in an encoder.

FIG. 8 is a flowchart of processing in a decoder.

FIG. 9 is a diagram for explaining an example of recording or transmission data in a conventional system (in a system in which the number of parameters is constant).

FIG. 10 shows an example of recorded or transmitted data in a conventional system.
FIG. 9 is a diagram for explaining (in the case of a system in which the number of scale factors is variable).

[Explanation of symbols]

WL word length, SF scale factor, 11
Time / frequency conversion circuit, 13 scale factor calculation circuit, 14 bit allocation calculation circuit, 15
Spectral signal quantization circuit, 17 masking calculation circuit, 18 parameter number calculation circuit, 19 data recording circuit

Claims (1)

(57) [Claims] 1. A quantized signal obtained by subjecting an audio signal to a plurality of blocks on a frequency axis for each predetermined time frame, performing block floating processing for each block, and quantizing data of each block by adaptive bit allocation. And, among the parameters for each block related to the block floating process, from the coded signal having parameters up to the band required for each time frame and the number information of the parameters for each time frame, An audio signal processing method for restoring an audio signal, comprising: extracting the number information of the parameter from the encoded signal; extracting the parameter from the encoded signal according to the number information of the parameter; Extracting the quantized signal from the encoded signal, Based on the number information and the parameter, an audio signal processing method characterized by restoring the audio signal from the quantized signal.