JP5119716B2 - Speech coding apparatus, speech coding method, and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To improve quality of reproduced speech, while maintaining simplicity of a device and a less transmission information amount, in an analysis-synthesis type speech encoding and decoding. <P>SOLUTION: A speech encoding device 1 makes an input speech signal subjected to predictive analysis, to be decomposed into a predictive coefficient and a residue signal. Information is compressed by the quantization of the predictive coefficient and a feature amount extraction of the residue signal. When the residue signal is pitch-like, for example, the predetermined number of markedly large samples for constituting the residue signal are selected, and only sufficient information for reproducing the samples is transmitted to a speech decoding device. As a result, while the speech decoding device restores the residue signal with high accuracy, information amount to be transmitted is reduced, and the quality of the reproduced speech is improved. <P>COPYRIGHT: (C)2009,JPO&amp;INPIT

Description

The present invention relates to a speech coding apparatus , a speech coding method , and a program that are required when executing analysis / synthesis speech compression / decompression.

  In the field of mobile communication, there is a need for a method for efficiently compressing and encoding speech at a low bit rate in order to cope with an increase in users and simplify terminal equipment. For example, as an 8 kbps speech coding method, there is a speech coding method shown in ITU-T recommendation G.729. The speech coding method according to the recommendation is basically a method of transmitting a speech signal after decomposing it into a prediction coefficient and a residual signal by predictive analysis. As prediction analysis, for example, linear prediction analysis and MLSA (Mel Log Spectrum Approximation) analysis (for example, refer to Non-Patent Document 1) are known.

Sei Imai, “Audio Signal Processing”, POD version, Morikita Publishing Co., Ltd., April 2005, p. 169-200

  In order to enable the above-described low bit rate communication in the analysis and synthesis type encoding and decoding, information on prediction coefficients and residual signals is efficiently transmitted from the speech encoding device side to the speech decoding device side. Ingenuity is required to communicate.

  The speech encoding method shown in the above-mentioned ITU-T recommendation G.729 is one of such devices. Certainly, this enables the speech decoding device to reproduce the speech content of the speech coding device user with a sound quality that the user can understand. However, today, the sound quality required for mobile communication is not limited to the quality of speech that can be transmitted, but it can withstand the use of transmitting accurate pronunciation for foreign language learning, for example. It is demanded.

  Note that the residual signal generated by the speech encoding device that is a transmitter is used as an excitation signal for speech reproduction in the speech decoding device that is a receiver. In other words, the term “residual signal” and the term “excitation signal” are used differently depending on the situation, and are the same in substance. Therefore, in the following, there is a case where the distinction between both terms is not limited.

  The present invention has been made in view of the above circumstances, and in the audio compression / decompression technique, the simplicity of the audio encoding device and the audio decoding device and the low bit rate are maintained at the same level as in the prior art. Aim to improve sound quality.

Therefore, when the residual signal is handled for each band, the speech encoding device obtains an appropriate value as the strength of the residual signal and transmits it to the speech decoding device, and particularly when the excitation signal has a pitch characteristic In addition to the residual signal samples that characterize the pitch length, other residual signal samples having a significant size are selected and the intensity of those samples is transmitted, thereby simplifying the device and reducing the bit rate. An object of the present invention is to provide a speech coding apparatus , speech coding method , and program capable of simultaneously achieving communication and high-quality sound reproduction.

The speech coding apparatus according to the present invention is:
A prediction analysis unit that decomposes a speech signal into a prediction signal and a residual signal that is a time series of residual samples;
A pitch length extraction unit for extracting a pitch length from the residual signal;
A reference residual sample that is a residual sample having the largest absolute value among the residual samples is identified, a residual sample maximum absolute value that is an absolute value of the reference residual sample is obtained, and the reference residual sample is obtained. A time zone corresponding to one pitch length starting from the corresponding time is divided into a predetermined number of segment time zones, and an average value of residual samples is determined by the residual sample maximum absolute value for each segment time zone. A feature amount extraction unit for obtaining a residual sample strength ratio by segment time, which is a value obtained by dividing,
An encoding unit that encodes the prediction coefficient, the pitch length, and the segmented time residual sample intensity ratio;
Is provided.

  According to the present invention, in the analysis and synthesis type audio signal communication, it is possible to simultaneously achieve the simplicity of the apparatus, the low bit rate communication, and the high sound quality reproduction.

  Hereinafter, embodiments of the present invention will be described. In describing the embodiments sequentially, in principle, the same members are denoted by the same reference numerals, and the description thereof may be omitted.

(Embodiment 1)
FIG. 1 is a functional configuration diagram of a speech encoding apparatus 1 according to Embodiment 1 of the present invention.

  As shown in the figure, the speech encoding apparatus 1 includes a microphone 11, an A / D conversion unit 13, a prediction analysis unit 15, a vector normalization unit 19, a scalar quantization unit 21, and a vector quantization unit 23. The synthesis filter calculation unit 25, the pseudo synthesis filter unit 27, the residual signal feature amount extraction unit 29, the residual signal restoration trial unit 31, the correction factor determination unit 33, the encoding unit 35, and the transmission unit 37 And comprising.

  The prediction analysis unit 15 includes a prediction analysis inverse filter calculator 17.

  The analog input audio signal input to the microphone 11 is converted into a digital input audio signal by the A / D conversion unit 13 by, for example, 16 kHz sampling, and then delivered to the prediction analysis unit 15. This digital input signal is composed of a time interval of about 12 to 20 ms, which is a subframe identified by the subscript j in the same main frame described later, and a continuous Z number of subframes, and is identified by the subscript i. It is divided into a time section called a main frame. For example, Z = 4 is preferable. A group of digital input signals is assumed to be composed of M main frames.

  Here, a group of digital input signals refers to a signal corresponding to a time length assumed to be collectively processed in the following description. The time length may be fixed or variable. In other words, M may be a fixed value or a variable value. If the storage capacity of a later-described storage unit 315 included in the later-described speech decoding apparatus 2 allows, the time length may be adjusted to, for example, the time when the user of the speech encoding apparatus 1 speaks at a breath.

  However, since the batch signal processing is performed based on the time length, the time length corresponds to the minimum time unit for communication from the speech encoding device 1 to the speech decoding device 2 described later. That is, the time length determines the lower limit of the delay time from an ideal real-time call. For the user of the speech encoding device 1 and the user of the speech decoding device 2 described later, it is considered that it is desirable from the viewpoint of usability that it is possible to communicate so as to make a call as close as possible to a real-time call. In that respect, the time length may be shortened as long as the CPU 311 and the wireless communication unit 317, which will be described later, operate, and an excessive load is applied to the processing speed.

  As described above, the main frame has a meaning as a unit of batch processing for convenience of understanding. However, the main frame has a meaning that it is a unit for performing vector quantization as will be described later.

The digital input signals handled in the following are S ₀ , ₀ ,..., S _{0, j} ,..., S _{0, Z−1} , S ₁ , _0,. , S _{i-1, Z-1} , S _{i, 0} , ..., S _{i, j} , ... S _{i, Z-1} , S _{i + 1} , ₀ , ..., S _{M-2, Z−1} , S _M−1 , ₀ ,..., S _{M−1, Z−1} , etc. are represented by subscripts i and j of 0 ≦ i ≦ M−1 and 0 ≦ j ≦ Z−1. I will decide.

Assume that each subframe includes L samples, and samples in the subframe identified by i and j are expressed in time order as S _{i, j} = {s _{i, j, 0} ,. , S _{i, j, t} ,..., S _{i, j, L-1} } (0 ≦ t ≦ L−1).

  In the present embodiment, in order to facilitate understanding of the invention, a mode of batch processing for a group of signals as described above is assumed as a method of various processing including communication processing. A known signal processing method or communication method, for example, a method in which some processing is batch processing and another processing is processing in units of smaller information may be adopted.

  The prediction analysis unit 15 performs Nth-order prediction analysis, preferably MLSA (Mel Log Spectrum Approximation) analysis, on the delivered digital audio signal. As a result, the prediction analysis unit 15 decomposes the digital audio signal into a prediction coefficient and a residual signal for each subframe. In actuality, the prediction analysis unit 15 first calculates a prediction coefficient such as an MLSA coefficient for each subframe, and then the prediction analysis inverse filter calculator 17 uses the prediction analysis inverse filter calculator 17 based on the prediction coefficient. A filter (not shown) is obtained, and a residual signal is obtained as a result of the digital audio signal being input to the prediction analysis inverse filter.

The prediction coefficients in the subframes identified by i and j are C _{i, j} = {c _{i, j, 0} ,..., c _{i, j, k ,. , J, N−1} } (0 ≦ k ≦ N−1).

  N is the order of the predictive analysis. When 16 kHz sampling is employed as the sampling rate as described above, N is preferably 15 to 20.

The residual signals in the subframes identified by i and j are, in chronological order, D _{i, j} = {d _{i, j, 0} , ..., d _{i, j, t} , ..., d _{i, j, L-1} } (0 ≦ t ≦ L−1).

  As described above, both the prediction coefficient and the residual signal are described as belonging to a specific subframe, but the time interval for the prediction analysis calculation actually performed by the prediction analysis unit 15 is longer than the subframe. It doesn't matter. The prediction coefficient and residual signal described as belonging to a certain subframe may be any prediction coefficient and residual signal representative of the subframe.

  For example, for prediction analysis in a subframe, in addition to the signal sample data actually included in the time zone corresponding to the subframe, the time zone corresponding to the second half of the previous subframe in the time series is calculated. Alternatively, signal sample data actually included in the signal and signal sample data actually included in the time zone corresponding to the first half of the next subframe may be used.

  The prediction coefficient calculated by the prediction analysis unit 15 is transferred to the vector normalization unit 19 as it is.

  The vector normalization part 19 produces | generates a coefficient vector by putting together a prediction coefficient for every main frame and every order so that it may explain in detail with reference to a flowchart later. Subsequently, the coefficient vector is normalized. Subsequently, the vector normalization unit 19 vector-quantizes the coefficient vector maximum absolute value, which is a scalar used for normalization, to the scalar quantization unit 21 and normalizes the normalized vector, which is a vector obtained as a result of normalization. Delivered to the unit 23.

  The scalar quantization unit 21 obtains a scalar quantized coefficient vector maximum absolute value by performing scalar quantization on the delivered coefficient vector maximum absolute value, and passes it to the synthesis filter calculating unit 25 and the encoding unit 35.

  The vector quantization unit 23 obtains a vector quantization normalized coefficient vector by vector quantization of the delivered normalization coefficient vector, and delivers it to the synthesis filter calculation unit 25 and the encoding unit 35.

  The synthesis filter calculation unit 25 performs inverse scalar quantization on the scalar quantization coefficient vector maximum absolute value delivered from the scalar quantization unit 21, and the vector quantization normalized coefficient vector delivered from the vector quantization unit 23. An inverse quantization coefficient vector is generated by multiplying the inverse vector quantized vector. Subsequently, the synthesis filter calculation unit 25 extracts a dequantized coefficient vector component to generate a pseudo prediction coefficient, and defines a specification as a synthesis filter of the pseudo synthesis filter unit 27 based on the pseudo prediction coefficient.

  The residual signal obtained by the prediction analysis unit 15 is delivered to the residual signal feature amount extraction unit 29.

  The residual signal feature quantity extraction unit 29 extracts a feature quantity, which is a quantity that characterizes the residual signal, from the delivered residual signal, and delivers it to the encoding unit 35 and the residual signal restoration trial unit 31. The details of the residual signal feature quantity extraction unit 29 will be described later.

  The residual signal restoration trial unit 31 acquires the feature amount from the residual signal feature amount extraction unit 29 and also acquires a part of the prediction coefficient calculated by the prediction analysis unit 15, and then uses the feature amount as a clue. A signal as close as possible to the residual signal obtained by the prediction analysis unit 15 is generated as a trial excitation signal. The generated trial excitation signal becomes an input signal to the pseudo synthesis filter unit 27. Details of the residual signal restoration trial unit 31 will be described later.

  The pseudo synthesis filter unit 27 is defined by the pseudo prediction coefficient generated by the synthesis filter calculation unit 25, and then receives the trial excitation signal generated by the residual signal restoration trial unit 31. Is generated.

  The correction factor determination unit 33 includes, as basic data for determining a correction policy, a trial reproduction audio signal generated by the pseudo synthesis filter unit 27 and a digital input audio signal obtained by the A / D conversion unit 13. Provided. The correction factor determination unit 33 compares the trial reproduction audio signal and the digital input audio signal to determine a correction policy. In the case of this embodiment, the correction policy is a correction policy related to signal strength. Therefore, in the following, the correction policy is generally referred to as a correction strength, or a specific signal strength value. The correction factor determination unit 33 passes the determined correction strength to the encoding unit 35.

  The encoding unit 35 receives the scalar quantization coefficient vector maximum absolute value from the scalar quantization unit 21, the vector quantization normalized coefficient vector from the vector quantization unit 23, the feature amount from the residual signal feature amount extraction unit 29, The correction strengths are received from the correction factor determination unit 33, and are collectively encoded by any known method, and the code obtained by the encoding is delivered to the transmission unit 37.

  The transmission unit 37 transmits the code delivered from the encoding unit 35 to the speech decoding apparatus 2 described later. In this embodiment, the transmission method is based on wireless communication. However, the transmission method may be based on various other communication methods such as wired or a combination of wired and wireless.

  FIG. 2 is a functional configuration diagram of the speech decoding apparatus 2 according to the present embodiment.

  The audio decoding device 2 includes a receiving unit 61, a decoding unit 63, a residual signal restoration unit 65, a D / A conversion unit 67, and a speaker 69, as illustrated.

  The speech decoding apparatus 2 further includes a synthesis filter calculation unit 25 and a pseudo synthesis filter unit 27 as indicated by a thick frame in the drawing. The synthesis filter calculation unit 25 and the pseudo synthesis filter unit 27 are the same as the synthesis filter calculation unit 25 and the pseudo synthesis filter unit 27 indicated by bold lines in FIG. is there.

  The receiving unit 61 of the speech decoding device 2 shown in FIG. 2 has a scalar quantization coefficient vector maximum absolute value, a vector quantization normalization coefficient vector, and features transmitted from the transmitting unit 37 of the speech encoding device 1 shown in FIG. A code in which the amount and the correction strength are encoded is received and delivered to the decoding unit 63 shown in FIG.

  The decoding unit 63 decodes the code delivered from the reception unit by a decoding method corresponding to the encoding method used by the encoding unit 35 of the speech encoding device 3, thereby allowing the scalar quantization coefficient vector maximum absolute value to be decoded. A vector quantization normalization coefficient vector, a feature amount, and a correction strength are generated. Among these, the decoding unit 63 passes the scalar quantization coefficient vector maximum absolute value and the vector quantization normalized coefficient vector to the synthesis filter calculation unit 25, and delivers the feature amount and the correction strength to the residual signal restoration unit 65. .

  The synthesis filter calculation unit 25 performs inverse scalar quantization on the scalar quantization coefficient vector maximum absolute value delivered from the decoding unit 63, and the vector quantization normalized coefficient vector delivered from the decoding unit 63 as an inverse vector quantum. An inverse quantized coefficient vector is generated by multiplying the converted vector. Subsequently, the synthesis filter calculation unit 25 extracts a dequantized coefficient vector component to generate a pseudo prediction coefficient, and defines a specification as a synthesis filter of the pseudo synthesis filter unit 27 based on the pseudo prediction coefficient.

  The residual signal restoration unit 65 obtains the feature amount and the correction strength from the decoding unit 63, and the prediction analysis unit 15 of the speech coding apparatus 1 obtains the feature amount as a clue while adding the correction strength. A signal expected to be a signal close to the residual signal is generated as a decoding excitation signal. The generated decoding excitation signal becomes an input signal to the pseudo synthesis filter unit 27. The residual signal restoration unit 65 performs an operation similar to the residual signal restoration trial unit 31 of the speech encoding apparatus 1. This point will be described later together with details of the residual signal restoration trial unit 31.

  The pseudo synthesis filter unit 27 generates a digital reproduction audio signal by being input with the decoding excitation signal generated by the residual signal restoration unit 65 after being defined by the pseudo prediction coefficient generated by the synthesis filter calculation unit 25 To do.

  The digital playback audio signal is converted into an analog playback audio signal by the D / A converter 67 and then sent to the speaker 69. Thus, the restored audio signal is emitted from the speaker 69 in a manner that can be heard by the human ear.

  FIG. 3 is a diagram showing a detailed functional configuration of the residual signal feature amount extraction unit 29 of the speech encoding device 1 of FIG.

The residual signal feature quantity extraction unit 29 incorporates the residual signals D _{i, j} = {d _{i, j, 0} ,..., D _{i, j, L-1} } of each delivered subframe. It is handed over to the total pitch discriminating unit 3, the band filter unit 115, the sample selecting unit 141, and in principle the pitch extracting unit 113. However, since the pitch extraction switch unit 111 is placed in front of the pitch extraction unit 113, the residual signals D _{i, j} are not necessarily delivered to the pitch extraction unit 113.

The total pitch determination unit 3 determines whether or not the pitch can be extracted from the residual signals _{Di, j} . Any known method may be used for the determination. For example, the normalized autocorrelation function for the residual signal D _{i, j}
C _REG (τ) = C (τ) / REG (τ)
(However,
C (τ) = d _{i, j, 0} × d _{i, j, τ} + ...
+ d _{i, j, L-1-τ} × d _{i, j, L-1}
And
REG (τ) = {(d _{i, j, 0} ² + ... + d _{i, j, L-1-τ} ² )
× (d _{i, j, τ} ² + ... + d _{i, j, L-1} ² )} ^0.5
It is. )
If C _REG (τ) has a maximum value greater than 0.5, for example, it is determined that the pitch can be extracted, and if C _REG (τ) does not have a maximum value greater than 0.5, the pitch It is determined that cannot be extracted.

When it is determined that the pitch can be extracted from the residual signals _{Di, j} , the overall pitch determination unit 3 sends a command to the pitch extraction switch unit 111 to close the switch. Upon receiving the command, the pitch extraction switch unit 111 closes the switch so that the residual signals D _{i, j} are sent to the pitch extraction unit 113.

On the other hand, when it is determined that the pitch cannot be extracted from the residual signals _{Di, j} , the overall pitch determination unit 3 sends a command to the pitch extraction switch unit 111 to open the switch. Upon receiving the command, the pitch extraction switch unit 111 opens the switch so that the residual signals D _{i, j} are not sent to the pitch extraction unit 113.

Pitch extraction unit 113, the residual signal D _{i, if j} is sent, said residue difference signal D _i, the pitch length of the _j P _i, obtaining the _j. The fact that the residual signal has been sent to the pitch extracting unit 113 means that the total pitch discriminating unit 5 that instructs opening / closing of the switch of the pitch extracting switch unit 111 can extract the pitch from the residual signal. It means that it has been determined. Therefore, the pitch extraction unit 113 should be able to obtain the pitch length of the transmitted residual signal. On the other hand, the pitch extraction unit 113 does nothing if the residual signal _{Di, j} has not been sent.

In order for the pitch extraction unit 113 to obtain the pitch lengths P _{i, j} of the residual signals D _{i, j} , any known method may be used. However, when the overall pitch discriminating unit 3 discriminates whether or not the pitch can be extracted using the above-mentioned standardized autocorrelation function C _REG (τ), C _REG ( The value of τ that gives a predetermined maximum value of τ) is easily obtained as a by-product. Further, in view of the definition of C _REG (τ), the value of τ is considered to correspond to the value of the pitch length. Therefore, it is easy for the pitch extraction unit 113 to acquire the value of τ from the total pitch determination unit 5 and use it as the pitch length P _{i, j as} it is.

  When the pitch extraction unit 113 determines the pitch length, the pitch extraction unit 113 passes the pitch length to the band-specific intensity determination unit 131 and the sample selection unit 141.

The band filter unit 115 uses the built-in first band filter 117, second band filter 119, third band filter 121,... To convert the residual signal D _{i, j} of each subframe to the remaining band for each band. Divide into difference signals. The band filter unit 115 divides the residual signal into bands 1 to 8, for example, band 1 is 0 to 1 kHz, band 2 is 1 to 2 kHz, band 3 is 2 to 3 kHz, band 4 is 3 to 4 kHz, band 5 Is preferably 4 to 5 kHz, band 6 is 5 kHz to 6 kHz, band 7 is 6 kHz to 7 kHz, and band 8 is 7 kHz to 8 kHz.

Residual signal D _i, band-by-band residual signal of the band 1 _{by j} is passed through the first band filter _{117 D i, j, 1 =} {d i, j, 1,0, ···, d i _{, J, 1, L-1} } are generated, and the residual signal D _{i, j} is passed through the second band filter 119, so that the band 2 residual signal D _{i, j, 2} = {d _{i , J, 2, 0} ,..., D _{i, j, 2, L-1} } are generated, and the residual signals D _{i, j} are passed through the third band filter 121, so that the band 3 is classified by band. Residual signals D _{i, j, 3} = {d _{i, j, 3,0} ,..., D _{i, j, 3, L-1} } are generated, and so on.

  Thus, here, the band filter unit 115 functions as a band-specific residual signal generation unit.

A variable for band identification is denoted by ω. For example, it is assumed that the signal generated by the first band filter 117 is a signal in the band of ω = 1, and the signal generated by the second band filter 119 is a signal in the band of ω = 2. The maximum value of ω is ω _Max . 1 ≦ ω ≦ ω _Max .

The generated band-specific residual signals D _{i, j, 1} of the band ₁ are the first band noise discriminator 125 in the band-by-band noise discriminating unit 123 and the first band intensity in the band-by-band intensity determining unit 131. The band-specific residual signals D _{i, j, 2} generated in the band 2 and handed over to the calculator 133 are determined by the second band noise discriminator 127 in the band-specific noise discriminating unit 123 and the band-specific strength determination. The same is applied to the second band intensity calculator 135 in the unit 131.

The first band noise discriminator 125 discriminates whether or not the band-by-band residual signal D _{i, j,} 1 in the band ₁ is noise, and notifies the flag generation unit 129 of the discrimination result. The second band noise discriminator 127 discriminates whether or not the band-specific residual signal D _{i, j,} 2 in band ₂ is noise, and notifies the flag generation unit 129 of the discrimination result. The same applies hereinafter.

The ω-band noise discriminator is obtained by any known method, and the band-by-band residual signal D _{i, j, ω} = {d _{i, j, ω, 0} , ..., d _{i, j, ω , L-1} } is determined as noise. For example, the normalized autocorrelation function given above as an example of the technique used to determine whether or not the overall pitch determination unit 3 can extract the pitch from the residual signals _{Di, j} It is preferable to use a calculation method.

That is, the normalized autocorrelation function for the residual signal _{Di, j, ω}
C _{REG, ω} (τ) = C _ω (τ) / REG _ω (τ)
(However,
C _ω (τ) = d _{i, j, ω, 0} × d _{i, j, ω, τ} + ...
+ d _{i, j, ω, L-1-τ} × d _{i, j, ω, L-1}
And
REG _ω (τ) = {(d _{i, j, ω, 0} ² + ... + d _{i, j, ω, L-1-τ} ² )
× (d _{i, j, ω, τ} ² + ... + d _{i, j, ω, L-1} ² )} ^0.5
It is. )
If C _{REG, ω} (τ) has a maximum value greater than 0.5, for example, it is determined that the pitch can be extracted and is not noise, and C _{REG, ω} (τ) is greater than 0.5. If it does not have a maximum value, it is determined that the pitch cannot be extracted and that it is noise.

The flag generation unit 129 determines whether the noise for the band 1 is determined from the noise determination unit 123 for each band, the determination result whether the noise is for the band 2, and the noise for the band ω. The determination result of whether or not, and the determination result of whether or not the noise is in the band ω _Max are transmitted. The flag generation unit 129 generates a flag for each band reflecting the determination result for each band. In other words, a flag is generated so that it can be seen from the flag whether or not the band-specific residual signal in the band corresponding to the flag is determined to be noise.

The band-specific intensity determining unit 131 receives the band-specific residual signals Di _{, j, and ω of} each band from the band filter unit 115, and further, the total pitch discriminating unit 5 extracts the pitch from the residual signal. When it is determined that it can be performed, the pitch length P _{i, j} is delivered from the pitch extraction unit 113.

The first band intensity calculator 133 in the band-specific intensity determination unit 131 calculates the intensity of the band-specific residual signal Di _{, j, 1 in} the band 1. The second band intensity calculator 135 calculates the intensity of the band-specific residual signal Di _{, j, 2 in} the band 2. The same applies hereinafter. For the ω-th band intensity calculator to calculate the intensity of the band-specific residual signals Di _{, j, and ω in} the band ω, any known technique may be used. For example, the following technique is suitable. is there.

First, the square of the residual signal for each band over time is integrated over time, and the square root is defined as the absolute intensity for each band H _{i, j, ω} . Since the band-specific residual signals D _{i, j, ω} are digital signals of D _{i, j, ω} = {d _{i, j, ω, 0} , ..., d _{i, j, ω, L-1} }. Specifically, the integral is substituted with a sum,
H _{i, j, ω} = sqrt (d _{i, j, ω, 0} ² + ... + d _{i, j, ω, Y-1} ² )
It is calculated as follows. However, the symbol sqrt means a square root.

In addition, Y is expected to be obtained more accurately by integrating in the longest possible range when the pitch length _{Pi, j} is not sent from the pitch extracting unit 113 to the band-specific intensity determining unit 131. , Y = L is preferable.

On the other hand, when the pitch length P _{i, j} is sent, the total pitch discriminating unit 3 discriminates that the residual signal D _{i, j} has a pitch characteristic as a whole. In order to obtain an accurate intensity without causing an error due to a phase shift, it is appropriate to set the time length for time integration to an integral multiple of the pitch length _{Pi, j} . And even in this case, it is expected that an accurate intensity can be obtained by integrating over as long a range as possible.
m × P _{i, j} ≦ L-1 <(m + 1) × P _{i, j}
It is preferable to obtain an integer m such that Y = m × P _{i, j} +1.

The band-specific absolute intensities H _{i, j, ω} are converted into relative intensities for each subframe, as will be described later using a flowchart. Hereinafter, this relative intensity is simply referred to as band-specific intensity h _{i, j, ω} .

The sample selection unit 141 determines that the pitch extraction switch 111 is closed, that is, the total pitch determination unit 3 can determine the pitch length, and the pitch extraction unit 113 determines the pitch length P _{i, j} . If it works, don't do anything else.

When the sample selection unit 141 operates, the samples d _{i, j, 0} ,... Included in the residual signal based on the delivered residual signals D _{i, j} and the pitch lengths P _{i, j. ..} , D _{i, j, L−1,} a sample characterizing the residual signal is selected according to a predetermined condition, and the speech decoding apparatus 2 in FIG. 2 can determine which sample is the sample Information is output as sample selection information.

That is, when the residual signal that the speech decoding apparatus 2 uses as a signal for excitation has a pitch property, the residual signal feature quantity extraction unit 29 of the speech encoding apparatus 1 in FIG. In addition to the residual signal samples that characterize the pitch lengths P _{i, j} , the sample selection unit 141 selects several residual signal samples that are considered to characterize the residual signal, such as having a significant size.

  The strengths of these samples are encoded by the encoding unit 35 and transmitted to the speech decoding apparatus of FIG.

  Thereby, compared with the case where all of the residual signal samples are encoded, the amount of information that the speech encoding apparatus should transmit to the speech decoding apparatus is small, which is suitable for low bit rate communication. In addition, since the speech decoding apparatus can generate a signal for excitation while referring to a sample other than the residual signal sample that characterizes the pitch length, the reproduced speech is the original speech input to the speech coding apparatus. Can be reproduced more faithfully.

  The predetermined condition when selecting a sample characterizing the residual signal will be described later.

Eventually, the residual signal feature quantity extraction unit 29 uses the band-specific strengths h _{i, j of} the respective bands obtained by the built-in band-specific strength determination unit 131 as well as the flags for the respective bands generated by the built-in flag generation unit 129. _{, Ω} are output as residual signal feature values. When the pitch extraction unit 113 obtains the pitch length _{Pi, j} , the residual signal feature quantity extraction unit 29 outputs the residual signal feature quantity including the pitch length and sample selection information as a feature quantity.

  As described above, since the residual signal is handled for each band, encoding according to the difference in the characteristics of the residual signal for each band in each band is performed, so that the quality of the decoded and reproduced sound is improved. In addition, since such a feature is roughly represented by a flag indicating a simple determination result of whether or not it is noisy, the simplicity of the speech encoding and speech decoding methods is maintained.

  In other words, the speech coding apparatus 1 can be simple in order to grasp the characteristics of the residual signal for each band from the viewpoint of whether or not it is roughly noisy. On the other hand, in the case of speech decoding, if there is a noise pulse train and pitch pulse train generation means and band division means, it is possible to restore the residual signal by band of all bands, and although it is simple, It is expected to improve the voice quality by handling it separately.

  FIG. 4 is a diagram showing a detailed functional configuration of the residual signal restoration trial unit 31 of the speech encoding device 1 of FIG.

The residual signal restoration trial unit 31 uses the flag for each band and the band-specific intensities h _{i, j, and ω} as the feature amounts, and the residual signal feature amount extraction unit 29 (FIGS. 1 and 3). ) Receive from. When the residual signal feature quantity includes the pitch length _{Pi, j} and sample selection information, the residual signal restoration trial unit 31 also receives the pitch length and the sample selection information.

The pitch pulse train generation unit 4 in the residual signal restoration trial unit 31 includes the pitch length and the sample selection information when the residual signal feature quantity includes the pitch length _{Pi, j} and the sample selection information. , A pitch pulse train is generated and passed to the band filter unit 115. On the other hand, if the residual signal feature quantity contains neither the pitch length P _{i, j} nor sample selection information, nothing is done.

How the pitch pulse train 4 generates the pitch pulse train based on the pitch length Pi _{, j} and the sample selection information will be described later.

  In FIG. 4, the band filter unit 115 drawn below the pitch pulse train generation unit 4 has the same function as the band filter unit 115 shown in FIG. However, the band filter unit 115 drawn below the pitch pulse train generation unit 4 aims to generate a band-specific pitch pulse sequence by dividing the pitch pulse train delivered from the pitch pulse train generation unit 4 into bands. That is, the band filter unit 115 drawn under the pitch pulse train generation unit 4 functions as a band-specific pitch pulse train generation unit.

The generated band-by-band pitch pulse train of each band is delivered to the band-by-band trial excitation signal generation unit 221. The band-specific trial excitation signal generator 221 includes a first band-specific trial excitation signal generator 223, a second band-specific trial excitation signal generator 225,... (Not shown), and ..., a trial excitation signal generator (not shown) for each ω _Max band trial. The first band-specific trial excitation signal generator 223 receives the band-specific pitch pulse train of band 1. The second band-specific trial excitation signal generator 225 receives the band-specific pitch pulse train of band 2. The same applies hereinafter.

  The noise pulse train generation unit 211 in the residual signal restoration trial unit 31 generates a noise pulse train and passes it to the band filter unit 115.

  The band filter unit 115 depicted below the noise pulse train generation unit 211 in FIG. 4 is the band filter unit 115 depicted in FIG. 3 and the band filter depicted below the pitch pulse train generation unit 4 in FIG. It has the same function as the unit 115. However, the band filter unit 115 drawn under the noise pulse train generation unit 211 is intended to generate a noise pulse train for each band by dividing the noise pulse train delivered from the noise pulse train generation unit 211 into bands. That is, the band filter unit 115 drawn under the noise pulse train generation unit 211 functions as a band-specific noise pulse train generation unit.

  The generated noise pulse train for each band of each band is delivered to the excitation signal generation unit 221 for each band trial. The first band-specific trial excitation signal generator 223 in the band-specific trial excitation signal generator 221 receives the band-specific noise pulse train of band 1. The second band-specific trial excitation signal generator 225 in the band-specific trial excitation signal generation unit 221 receives the band-specific noise pulse train of band 2. The same applies hereinafter.

  The band-specific residual signal strength of each band and the flag for each band passed to the residual signal restoration trial unit 31 as the feature amount are sent to the band-specific trial excitation signal generation unit 221. At that time, the intensity of the band-specific residual signal in the band 1 and the flag for the band 1 are sent to the first band-specific trial excitation signal generator 223 in the band-specific trial excitation signal generator 221. The strength of the band-specific residual signal in the band 2 and the flag for the band 2 are sent to the second band-specific trial excitation signal generator 225 in the band-specific trial excitation signal generation unit 221. The same applies hereinafter.

  After all, the ω-th band trial excitation signal generator in the band-specific trial excitation signal generator 223 includes a flag for the band ω, the intensity of the residual signal for each band in the band ω, and the band of the band ω. When a different noise pulse train is delivered, and when the pitch length is included in the feature quantity, a per-band pitch pulse train of the bandwidth ω is also delivered. The ω-band trial excitation signal generator is a signal that is expected to be suitable as a component of the band ω of the trial excitation signal from these three to four types of information. Is generated.

  Details of the generation process will be described later with reference to a flowchart. In general, the ω-band trial excitation signal generator generates a signal when the flag for band ω indicates that the band-by-band residual signal of band ω has noisy properties. Since it is appropriate to have noise characteristics, the target signal is generated by multiplying the noise pulse train for each band in the band ω by the intensity of the residual signal in the band ω. On the other hand, if the flag for the band ω indicates that the band-specific residual signal of the band ω does not have noise characteristics, it is appropriate that the generated signal also has no noise characteristics. Therefore, the trial excitation signal generator for each ω-th band generates a target signal by multiplying the band-by-band pitch pulse train of the band ω by the intensity of the residual signal in the band ω.

  However, even if the flag suggests that the corresponding band does not have noise characteristics, if the residual signal feature quantity does not include the pitch length, the residual signal restoration trial unit 31 determines the pitch. Since a pulse train for each band as well as a pulse train is not generated, care must be taken that there is no attempt to create a target signal having no noise characteristics. However, this is a situation that is unlikely to occur in principle because there are periodic residual signals in each band even though the residual signal as a whole has no periodicity. In other words, it can be said that it is merely necessary to properly prepare exception handling in calculation.

  As will be clear from the signal generation process described in detail later, the intensity of the noise pulse train for each band, the pitch pulse train for each band, and the intensity of the residual signal for each band have been subjected to a certain standardization. Even if operations such as multiplication are performed on each other, only a certain relative value can be obtained.

  However, in this embodiment, it is important to handle the absolute value of the signal. Therefore, the band-specific trial excitation signal generation unit 221 needs to generate a band-specific trial excitation signal for each band by multiplying the above-described relative value by a specific value as a reference. Since the feedback by the correction factor determination unit 33 is applied later, the specific value serving as the reference does not need to be determined strictly. Therefore, a predetermined fixed value may be determined in advance based on an empirical rule, but the band-specific trial excitation signal generation unit 221 determines the specific value serving as the reference as a dotted arrow in FIG. 1 and FIG. As represented by the dotted line arrow, it is more preferable to determine a part of the prediction coefficient obtained by the prediction analysis unit 15 as a clue. This specific method will be described later.

  The band-specific trial excitation signal generated by the band-specific trial excitation signal generation unit 221 is delivered to the trial excitation signal generation unit 227. The trial excitation signal generation unit 227 generates a trial excitation signal by superimposing the passed band-specific trial excitation signals, and outputs the signal.

  The residual signal restoration unit 65 (FIG. 2) included in the speech decoding apparatus 2 is very similar to the residual signal restoration trial unit 31 described with reference to FIG. 4 as described above. This is because both have the common purpose of generating excitation signals from feature quantities. However, the residual signal restoration trial unit 65 receives the correction strength determined by the correction factor determination unit 33 (FIG. 1), and generates an excitation signal while reflecting the correction strength. Therefore, the signal output from the residual signal restoration unit 65 is better approximated to the signal output from the residual signal restoration trial unit 31 with respect to the residual signal obtained by the prediction analysis unit 15 (FIG. 1). Signal. Further, in the residual signal restoration unit 65, smoothing processing is performed on the signal strength between subframes. Details of the operation of the residual signal restoration trial unit 65 will be described later.

  The speech encoding apparatus 1 and the speech decoding apparatus 2 according to the present embodiment that have been described with reference to FIGS. 1 to 4 which are functional configuration diagrams so far are physically considered in consideration of ease of use for the user. Thus, the speech encoding / decoding device 5 according to the present embodiment is realized by integrating the functions of both devices.

  FIG. 5 shows a speech encoding / decoding device 5 according to this embodiment. As the voice encoding / decoding device 5, for example, a mobile phone is assumed.

  The speech encoding / decoding device 5 includes a microphone 11 already shown in FIG. 1 and a speaker 69 already shown in FIG. The apparatus further includes an antenna 325 and operation keys 327. The apparatus includes a CPU (Central Processing Unit) 311, a ROM (Read Only Memory) 313, a storage unit 315, an audio processing unit 319, a wireless communication unit 317, and an operation connected to each other via a system bus 323. A key input processing unit 321. The storage unit 315 includes, for example, a RAM (Random Access Memory) 329 and a hard disk 331.

  The ROM 313 stores an operation program for voice encoding and decoding. Also stored are various data to be referred to in the operation program, such as an initial set of representative vectors necessary for vector quantization.

  The CPU 311 operates according to the operation program. Then, the CPU 311 performs numerical computation while appropriately exchanging data between a built-in register (not shown) and the storage unit 315, and sends the speech coding shown in FIG. The function as the apparatus 1 and the speech decoding apparatus 2 shown in FIG. 2 is exhibited. At that time, the CPU 311 exchanges data with the voice processing unit 319, the wireless communication unit 317, and the operation key input processing unit 321 as necessary.

  The voice processing unit 319 in FIG. 5 can operate as the A / D conversion unit 13 in FIG. 1 and the D / A conversion unit 67 in FIG. The wireless communication unit 317 can operate as the transmission unit 37 in FIG. 1 and the reception unit 61 in FIG. The transmission / reception of the code is basically performed by wireless communication using the antenna 325 of FIG. 5, but may be performed by another method, for example, wired communication. The operation key input processing unit 321 receives an operation signal from the operation key 327 and transmits a key code signal corresponding to the operation signal to the CPU 311. The operation key 327 is used to specify the voice encoding / decoding device 5 to be a communication partner, that is, for example, to input a so-called telephone number in the case of a mobile phone, and basically, various types that have been set. It may be used to change matters according to user preferences.

(About quantization)
The speech coding apparatus 1 (FIG. 1) according to the present embodiment helps to realize low bit rate communication by reducing the amount of information to be transmitted to the speech decoding apparatus 2 (FIG. 2) by quantizing the prediction coefficient. And In the present embodiment, the quantization uses both the scalar quantization by the scalar quantization unit 21 and the vector quantization by the vector quantization unit 23 in FIG. Hereinafter, how such quantization is performed in the present embodiment will be described.

  In the present embodiment, vector quantization is performed after collecting a number of prediction coefficients that are inherently scalars and constructing a vector having each prediction coefficient as a component. However, instead of directly quantizing the vector thus configured, a vector normalization process is inserted to extract a value to be subjected to scalar quantization. Thus, the efficiency of quantization improves by combining both types of quantization.

FIG. 6A summarizes a time series of prediction coefficients in a table. As described above, the main frame is identified by the suffix i, and the subframes in the same main frame are identified by the suffix j. Each main frame includes Z subframes numbered 0,..., J,. The prediction coefficients C _{i, j} for the residual signal of the j-th subframe in the i-th main frame, as already described, and as shown in FIG. 6A as each column, C _{i, j} = {ci _{, j, 0} , ..., ci _{, j, k} , ..., ci _{, j, N-1} } (0≤k≤N-1) 0 to N A collection of -1st order prediction coefficients.

Here, a coefficient vector is configured with a total of Z prediction coefficients included in the same main frame and having the same order as components. That is, as shown in FIG. 6A surrounded by a dotted frame, the coefficient vector is specified by a subscript i representing the main frame and a subscript k representing the order, so that V _{i and k} are represented. The ingredients are
V _{i, k} = {ci _{, 0, k} , ..., ci _{, j, k} , ..., ci _{, Z-1, k} }.

  That is, the subframe means a time interval corresponding to prediction analysis, whereas the main frame means a time interval constituting a coefficient vector.

The coefficient vectors V _{i, k} configured in this way are schematically represented as shown in FIG. 6B in the Z-dimensional space. There are various specific vector quantization methods, and any known method may be used in the present embodiment. In any method, in principle, a set of a finite number of representative vectors, each assigned a number, is prepared, and a given vector is approximated by the nearest representative vector or a linear combination thereof. In other words, the given vector is converted into a number assigned to the representative vector. For convenience of handling, numbers are often collected as a table.

For example, in FIG. 6B, the illustrated coefficient vector V _{i, k} is assumed to be close to the vector represented by the dotted line among the representative vectors. At this time, the vector represented by the dotted line is the quantization coefficient vector q [V] _{i, k} . Further, it can be said that the difference between V _{i, k} and q [V] _{i, k} is information omitted by vector quantization.

As described above, the coefficient vectors are collected in a table when vector quantization is performed. Therefore, it can be said that the quantization coefficient vector is not a vector at least in appearance. However, if a numerical value described in the table is given a meaning as a vector, that is, inverse quantization is performed, quantized coefficient vectors q [V] _{i, k} are obtained. Therefore, in order to avoid unnecessary confusion, in the following, both the result obtained as a table by applying quantization to the coefficient vector and the vector obtained by applying inverse quantization to the table are both: These are referred to as quantization coefficient vectors q [V] _{i, k} .

  In this embodiment, it is highly efficient in terms of information compression that the filter vectors of the same dimension are combined over the main frame, that is, consecutive Z subframes, to form a coefficient vector.

This is because the coefficient vectors V _{i, k} are close to each other in the time zone in which the speech is in a steady state, and therefore, the distribution of the coefficient vectors V _{i, k} obtained from the entire speech duration is This is because a large bias occurs. In general, when vector quantization is applied to a set of vectors with a biased distribution, information compression efficiency is good.

(Predictive analysis procedure)
Hereinafter, the prediction analysis performed by the prediction analysis unit 15 of FIG. 1 will be described with reference to the flowchart shown in FIG. As prediction analysis, for example, linear prediction analysis and MLSA (Mel Log Spectrum Approximation) analysis are known.

The storage unit 315 (FIG. 5) already has a digital input audio signal S _{i, j} = {s _{i, j, 0} ,..., S _{i, j, L-1} } (0 ≦ i ≦ M−1). ) Is stored. The CPU 311 (FIG. 5) uses a built-in counter register (not shown) for storing the main frame counter i, and sets i = 0 as an initial value (step S7 in FIG. 7).

  The CPU 311 uses another built-in counter register (not shown) for storing the subframe counter j, and sets j = 0 as an initial value (step S111).

The CPU 311 loads the input audio signal S _{i, j} = {si _{, j, 0} ,..., Si _{, j, L-1} } from the storage unit 315 to a built-in general-purpose register (not shown). (Step S113).

The CPU 311 performs prediction analysis on the input speech signal S _{i, j} to calculate a prediction coefficient C _{i, j} = {ci _{, j, 0} ,..., C _{i, j, N−1} }. (Step S115). N is the order of predictive analysis. As the predictive analysis, for example, MLSA analysis is preferably adopted.

The CPU 311 stores the calculated prediction coefficient C _{i, j} in the storage unit 315 (step S117).

The CPU 311 calculates an inverse filter coefficient Inv [C _{i, j} ] for prediction analysis from the prediction coefficient C _{i, j} by any known method. The inverse filter for prediction analysis 17 (FIG. 1) is defined by the inverse filter coefficient Inv [C _{i, j} ]. In other words, the specifications of the prediction analysis inverse filter 17 are determined, or the filter is generated (step S119).

The CPU 311 performs a calculation corresponding to passing the input speech signal S _{i, j} through the defined inverse filter 17 for predictive analysis, so that the residual signal D _{i, j} = {d _{i, j, 0} , ..., D _{i, j, L−1} } are obtained (step S121).

The CPU 311 stores the obtained residual signal D _{i, j} in the storage unit 315 (step S123).

  The CPU 311 determines whether or not the subframe counter j has reached Z-1 (step S125). If it is determined that it has been reached (step S125; Yes), the process proceeds to step S129. On the other hand, if it is determined that it has not been reached (step S125; No), j is incremented by 1 in order to perform processing on the input audio signal of the next subframe in the same main frame (step S127). ), And the process after step S113 is repeated.

  In step S129, the CPU 311 determines whether or not the main frame counter i has reached M-1. If it is determined that it has been reached (step S129; Yes), the process is terminated. If it is determined that it has not been reached (step S129; No), i is incremented by 1 (step S131), and the processing after step S111 is performed in order to perform processing for the input audio signal of the next mainframe. repeat.

(Vector quantization procedure)
Hereinafter, the procedure of vector normalization, scalar quantization, and vector quantization performed by the vector normalization unit 19, the scalar quantization unit 21, and the vector quantization unit 23 of FIG. 1 will be described with reference to the flowchart shown in FIG. However, it will be explained.

The prediction coefficients C _{i, j} (0 ≦ i ≦ M−1, 0 ≦ j ≦ Z−1) have already been calculated as coefficient vectors V _{i, k} (0 ≦ i ≦ M−1, 0 as shown in FIG. 6). ≦ k ≦ N−1) and are stored in the storage unit 315.

  The CPU 311 sets the main frame counter i to i = 0 (step S8).

  The CPU 311 sets the order counter k to k = 0 (step S161).

The CPU 311 loads the coefficient vector V _{i, k} = {c _{i, 0, k} ,..., C _{i, Z−1, k} } from the storage unit 315 to the register (step S163), c _{i, 0, k} ,..., c _{i, Z−1, k} are identified with the maximum absolute value, and the absolute value is set as the coefficient vector maximum absolute value Max [c] _{i, k} (step S165). .

The CPU 311 scalar quantizes the coefficient vector maximum absolute value Max [c] _{i, k} by any known method (step S167), and the scalar quantization coefficient vector maximum absolute value q [Max [c]] obtained as a result thereof. _{i and k} are stored in the storage unit 315 (step S169).

The CPU 311 determines the normalized coefficient vector n [c] _{i, k} = {n [c] _{i, 0, k} ,... From the coefficient vector V _{i, k} and the coefficient vector maximum absolute value Max [c] _{i, k.} , N [c] _{i, Z-1, k} }, n [c] _{i, k} = {c _{i, 0, k} / Max [c] _{i, k} , ..., c _{i, Z-1 , K} / Max [c] _{i, k} } to calculate (step S171).

The CPU 311 vector-quantizes the normalized coefficient vector n [c] _{i, k} by any known method (step S173), and the resulting vector quantization coefficient vector q [n [c]] _{i, k} = {q [n [c]] _{i, 0, k} ,..., q [n [c]] _{i, Z-1, k} } are stored in the storage unit 315 (step S175).

  The CPU 311 determines whether k has reached N−1 (step S177). If it is determined that it has been reached (step S177; Yes), the process proceeds to step S181. If it is determined that it has not been reached (step S177; No), k is incremented by 1 (step S179), and the process returns to step S163.

  In step S181, the CPU 311 determines whether i has reached M-1. If it is determined that it has been reached (step S181; Yes), the process is terminated. If it is determined that it has not been reached (step S181; No), i is increased by 1 (step S183), and the process returns to step S161.

(Procedure for generating feature value from residual signal)
In the following, in the speech coding apparatus 2 according to the present embodiment, the residual signal feature quantity extraction unit 29 shown in FIGS. 1 and 3 performs the pitch length, the sample selection information, the flag, the intensity by band, from the residual signal. The procedure for generating is described with reference to the flowchart shown in FIG.

As a premise, the residual signals D _{i, j} = {d _{i, j, 0} , ..., d _{i, j, L-1} } (0≤i≤M-1, 0≤j≤Z- 1) is obtained and stored in the storage unit 315.

  The CPU 311 sets the main frame counter i to i = 0 (step S9).

  The CPU 311 sets the subframe counter j to j = 0 (step S211).

The CPU 311 loads the residual signal D _{i, j} = {d _{i, j, 0} ,..., D _{i, j, L-1} } from the storage unit 315 to the register (step S213).

The CPU 311 determines whether or not the pitch length can be extracted from the residual signals D _{i, j} (step S215). For this discrimination, for example, a standardized autocorrelation function is used as described for the total pitch discrimination unit 5 in the residual signal feature amount extraction unit 29 with reference to FIG.

If it is determined that the pitch length can be extracted (step S215; Yes), the CPU 311 obtains the pitch length P _{i, j} and sample selection information (step S217).

The pitch length P _{i, j} is, for example, an autocorrelation standardized by the total pitch discriminating unit 3 as described for the pitch extracting unit 113 in the residual signal feature amount extracting unit 29 with reference to FIG. It is obtained as a by-product when it is determined whether or not a pitch can be extracted using a function.

  An example of how to obtain the specimen selection information will be described later.

Thereafter, the CPU 311 stores the obtained pitch length _{Pi, j} and sample selection information in the storage unit 315 (step S219), and then proceeds to step S221.

  On the other hand, when it is determined that the pitch length cannot be extracted (step S215; No), the process immediately proceeds to step S221.

  In step S221, the CPU 321 sets the band identification variable ω to ω = 1.

The CPU 311 functions as a band filter unit 115 (FIG. 3) as a band-specific residual signal generation unit, so that the band-specific residual signals D _{i, j, ω} = {d _{i, j, ω, 0} ,..., D _{i, j, ω, L−1} } are generated (step S223 in FIG. 9).

The CPU 311 functions as the band-specific noise determination unit 123 (FIG. 3) and the flag generation unit 129 to determine whether or not the band-specific residual signals Di _{, j, and ω} are noises and reflect the results. The flags Flag _{i, j, and ω} for the band ω are generated (step S225 in FIG. 9) and stored in the storage unit 315 (step S227). The values that the flag can take as variables will be described later.

The CPU 311 functions as the band-by-band intensity determining unit 131 (FIG. 3) to obtain band-by-band absolute intensities H _{i, j, ω} of the band-by-band residual signals D _{i, j, ω} (step S229 in FIG. 9). . This method will be described later.

The CPU 311 determines whether or not ω has reached ω _Max (step S231).

If it is determined that ω has reached ω _Max (step S231; Yes), the process proceeds to step S235. If it is determined that ω has not reached ω _Max (step S231; No), ω is increased by 1 (step S233), and the process returns to step S223.

When step S235 is reached, since the loop processing (step S231, step S233, etc.) for ω in the subframe is completed, the CPU 311 calculates the band-specific intensities h _{i, j, ω} that are relative values. Can be sought. The CPU 311 obtains the band-specific intensities h _{i, j, ω} (step S235) and stores them in the storage unit 315 (step S237). A specific method for _obtaining the band-specific intensities h _{i, j, ω} will be described later.

  The CPU 311 determines whether j has reached Z-1 (step S239). If it is determined that it has been reached (step S239; Yes), the process proceeds to step S243. If it is determined that it has not been reached (step S239; No), j is incremented by 1 (step S241), and the process returns to step S213.

  In step S243, the CPU 311 determines whether i has reached M-1. If it is determined that it has been reached (step S243; Yes), the process is terminated. If it is determined that it has not been reached (step S243; No), i is increased by 1 (step S245), and the process returns to step S211.

  The process for generating the sample selection information performed in step S217 of FIG. 9 will be described with reference to the flowchart shown in FIG. 10 and the schematic diagram of the residual signal shown in FIG.

FIG. 11A is a schematic diagram of residual signals D _{i, j} in subframes identified by i and j. The values of the samples d _{i, j, 0} ,..., D _{i, j, L-1 in} the residual signal are shown as time series.

The CPU 311 selects a reference residual sample di _{, j, u (0)} which is a sample having the maximum absolute value from among these samples di _{, j, 0} ,..., Di _{, j, L-1. )} Is specified (step S10 in FIG. 10).

It is possible to characterize the residual signal from the samples within the range of the pitch length Pi _{, j in the} time series from the reference residual sample di _{, j, u (0)} identified in this way. Select the expected specimen. Such a range in which a sample can be selected is hereinafter referred to as a search target section.

In order to secure the search target section, the reference residual samples d _{i, j, u (0)} are pitch lengths P _i from the last samples d _{i, j, L-1} on the time series. _{, J} or more must be present. That is, the inequality u (0) ≦ L-1-P _{i, j} needs to be satisfied. Therefore, if the reference residual sample selected as described above does not satisfy this inequality, the absolute value of the samples d _{i, j, 0} ,..., D _{i, j, L−1} is The second sample is specified again as the reference residual sample d _{i, j, u (0) (} step S911 in FIG. 10). If the reference residual sample re-specified in this way also does not satisfy the inequality, the sample with the third absolute value is specified again as the reference residual sample. Hereinafter, similarly, the re-decision of the reference residual sample is repeated until the inequality is satisfied.

FIG. 11B is an enlarged view of the search target section in FIG. In the search section, the specimen is, as _{illustrated, d i, j, u (} 0), ···, d i, j, u (0) + Pi, of _j-1, total P _{i, j-number} Exists.

These P _i, d _i from the _{j samples, j, u (0)} except for the _{d i, j, u (0} ) +1, ···, d i, j, u (0) + Pi, j Sort _-1 in descending order of absolute value by any known method. Then, the sample d _i from the beginning to σ _{th, j, u (1),} ···, d i, j, for _{u (σ), u (1} ) -u (0) and d _{i, j, u (1)} / | d _{i, j, u (0)} |, ..., u (σ) -u (0) and d _{i, j, u (1)} / | d _{i, j, u (0)} | Is obtained (step S913).

In principle, σ can be set to _{Pi and j−1 at} maximum. However, the significance of generating sample selection information is to select a small number of samples that are expected to characterize the residual signal and to adapt to low bit rate communication. Therefore, the number of samples to be selected is determined by comparing the given information transmission allowance with the quality of reproduced speech to be achieved.

  Note that the number of specimens may be determined in advance, but is not necessarily a fixed value. For example, when the encoding unit 35 in FIG. 1 employs an encoding method in which the information compression rate does not become constant, such as entropy encoding, there is room in the transmittable capacity because the information compression rate happens to be high. The number of samples may be determined dynamically, such as selecting a large number of samples in a certain time zone.

  As shown in FIG. 11C, the relative position of the selected sample with respect to the reference residual sample in time series, and a value obtained by normalizing the selected sample with the absolute value of the reference residual sample are: The sample selection information is obtained (step S915 in FIG. 10), and the process ends.

  In order to facilitate understanding, an outline of processing in which the speech decoding apparatus 2 of FIG. 2 generates a decoding excitation signal based on the sample selection information will be described with reference to FIG.

  The speech decoding apparatus 2 of FIG. 2 that has received the sample selection information from the speech encoding apparatus 1 of FIG. 1 includes the relative position and relative sample value of the sample selected as described above with respect to the reference residual sample. Therefore, as shown in FIG. 12A, it is possible to generate a signal of one pitch starting from the reference residual sample.

  Subsequently, as shown in FIG. 12B, the speech decoding apparatus 2 generates a decoding excitation signal by repeatedly connecting the signals for one pitch in time series.

At the same time, the reference residual sample and the selected sample are subjected to enlargement or reduction so that the size of the reference residual sample becomes sqrt (P _{i, j} ) as described later.

  FIG. 12C is a schematic diagram showing the original residual signal, as in FIG. As is clear from comparing FIG. 12B with FIG. 12C, the decoding excitation signal qualitatively selects a small number of conspicuous samples from the original residual signal, and selects them in units of pitch length. It is generated by repeating.

FIG. 13 is a flowchart showing the processing for generating the flags Flag _{i, j, and ω} for the band ω performed in step S225 of FIG.

CPU311, by functioning as a band-by-band noise determination unit 123 of FIG. 3, the band-by-band residual signal D _i of band _{ω, j, ω = {d} i, j, ω, 0, ···, d i, It is determined whether _{j, ω, L−1} } is noise (step S13). As a technique for discrimination, for example, it is preferable to use a technique by calculating a standardized autocorrelation function as already described as the operation of the ω-th band noise discriminator in FIG.

If it is determined that the noise is detected (step S13; Yes), the CPU 311 sets the variables Flag _{i, j, and ω} to “UV” (step S261), and ends the process. When it is determined that it is not noise (step S13; No), the CPU 311 sets the variables Flag _{i, j, and ω} to “V” (step S263), and ends the process.

FIG. 14 is a flowchart showing processing for _obtaining the band-specific absolute intensities H _{i, j, ω} of the band-specific residual signals D _{i, j, ω} performed in step S229 of FIG.

The CPU 311 searches the storage unit 315 and determines whether or not the pitch length P _{i, j} is stored in the storage unit 315 (step S14). If the pitch length P _{i, j} is stored in the storage unit 315, this is due to step S219 in FIG. 9, and the fact that it has undergone step S219 indicates that the residual signal D _{i, j} has been stored in step S215. Means that it is determined that a pitch-like property exists as a whole. If the pitch length P _{i, j} is not stored in the storage unit 315, it means that it is determined in step S215 that the residual signal D _{i, j} does not have pitch characteristics as a whole.

When it is determined that the pitch length P _{i, j} is stored in the storage unit 315 (step S14; Yes), for example, over the predetermined time, as described for the band-specific strength determination unit 131 in FIG. After the square of the residual signal for each band is time-integrated, the square root is set as the absolute intensity for each band H _{i, j, ω} (step S271). Further, as described above, the fact that the process has proceeded to this step means that the residual signals D _{i, j} have a pitch-like nature as a whole, and this is also already determined by the band-specific intensity determining unit of FIG. As mentioned in the description of 131, the predetermined time is preferably an integral multiple of _{Pi, j} and is as long as possible. Thereafter, the process ends.

When it is determined that the pitch length P _{i, j} is not stored in the storage unit 315 (step S14; No), as already mentioned in the description of the band-specific strength determination unit 131 in FIG. Based on the integration over the entire duration of the band-specific residual signals D _{i, j, ω} , band-specific absolute intensities H _{i, j, ω} are obtained (step S273). Thereafter, the process ends.

FIG. 14 is a flowchart showing a process for _{obtaining the} absolute intensity H _{i, j, ω} by band indicating the absolute magnitude of the intensity, whereas FIG. 15 is performed in step S235 of FIG. It is a flowchart which shows the process which produces _| generates the intensity | strength classified by band _{hi, j, and} which is a relative intensity | strength.

The band-specific intensities h _{i, j, ω} are relative values of the intensity when the maximum band-specific absolute intensity H _{i, j, ω} is 1 in the subframe specified by i and j.

The CPU 311 specifies the highest value among the band-specific absolute intensities H _{i, j, 1} ,..., H _{i, j, ωMax} of the band-specific residual signals D _{i, j, ω} , Are set to the band-specific absolute intensity maximum values _{Hi, j, Max} (step S15).

  The CPU 311 sets the band identification variable ω to ω = 1 (step S281).

The CPU 311 obtains the band-specific intensities h _{i, j, ω} from h _{i, j, ω} = _{Hi, j, ω} / Hi _{, j, Max} (step S283).

The CPU 311 determines whether or not ω has reached ω _Max (step S285). If it is determined that it has been reached (step S285; Yes), the process is terminated. If it is determined that it has not been reached (step S285; No), ω is increased by 1 (step S287), and step S283 is repeated.

(Procedure for generating a trial excitation signal from features)
In the following, in the speech encoding apparatus 1 according to the present embodiment, the trial excitation signal based on the pitch length, the sample selection information, the flag, and the intensity for each band, which is performed by the residual signal restoration trial unit 31 illustrated in FIGS. 1 and 4. The procedure for generating is described with reference to the flowchart shown in FIG.

As a premise, it is assumed that the flags Flag _{i, j, ω} and the band-specific intensities h _{i, j, ω} have already been obtained and stored in the storage unit 315 (0 ≦ i ≦ M−1, 0 ≦ j ≦ Z−1, 1 ≦ ω ≦ ω _Max ). If the pitch length P _{i, j} can be extracted from D _{i, j} (step S215 in FIG. 9; Yes), the pitch length P _{i, j} and the sample selection information are also obtained and stored in the storage unit. It is assumed that it is stored in 315.

  The CPU 311 sets the main frame counter i to i = 0 (step S16).

  The CPU 311 sets the subframe counter j to j = 0 (step S311).

The CPU 311 searches the storage unit 315 to determine whether or not the pitch length P _{i, j} is stored in the storage unit 315 (step S313).

When it is determined that the pitch length P _{i, j} is stored in the storage unit 315 (step S313; Yes), the CPU 311 loads the pitch length P _{i, j} and sample selection information from the storage unit 315 to the register. (Step S315). Subsequently, the CPU 311 functions as the pitch pulse train generation unit 4 and the band filter unit 115 in FIG. 4, so that the band-specific pitch pulse trains Ppt _{i, j, ω} = {ppt _i based on the pitch lengths P _{i, j. , J, ω, 0} ,..., Ppt _{i, j, ω, L−1} } are generated (step S317 in FIG. 16), and the process proceeds to step S319. An example of a specific method for obtaining the band-specific pitch pulse train Ppt _{i, j, ω} will be described later.

If it is determined that the pitch length P _{i, j} is not stored in the storage unit 315 (step S313; No), the process immediately proceeds to step S319.

In step S319, the CPU 311 functions as the noise pulse train generation unit 211 and the band filter unit 115 in FIG. 6, so that the noise pulse trains for each band Rpt _{i, j, ω} = {rpt _{i, j, ω, 0} ,. , Rpt _{i, j, ω, L-1} }. An example of a specific method for obtaining the band-specific noise pulse trains Rpt _{i, j, ω} will be described later.

The CPU 311 changes the trial excitation signal Ex _{i, j} = {ex _{i, j, 0} , ..., ex _{i, j, L-1} } to Ex _{i, j} = {0, ..., 0}. Initialization is performed (step S321).

  The CPU 311 sets the band identification variable ω to ω = 1 (step S323).

The CPU 311 loads the flags Flag _{i, j, ω} and the band-specific strengths h _{i, j, ω} from the storage unit 315 to the register (step S325).

CPU311, by functioning as a band-by-band trial excitation signal generation unit 221 of FIG. 4, the excitation signal band by trial _{Ex i, j, ω = {} ex i, j, ω, 0, ···, ex i _{, J, ω, L−1} } are generated (step S327 in FIG. 16). An example of a specific method for obtaining the band-specific trial excitation signals Ex _{i, j, ω} will be described later.

The CPU 311 updates the trial excitation signal Ex _{i, j} to Ex _{i, j} + Ex _{i, j, ω} (step S329).

The CPU 311 determines whether or not ω has reached ω _Max (step S331). If it is determined that it has been reached (step S331; Yes), the process proceeds to step S335. If it is determined that the value has not been reached (step S331; No), ω is increased by 1 (step S333), and the process returns to step S325.

In step S335, the CPU 311 stores in the storage unit 315 the trial excitation signal Ex _{i, j} which is the sum of the band-specific trial excitation signals for all bands at this time.

  The CPU 311 determines whether j has reached Z-1 (step S337). If it is determined that it has been reached (step S337; Yes), the process proceeds to step S341. If it is determined that it has not been reached (step S337; No), j is incremented by 1 (step S339), and the process returns to step S313.

  In step S341, the CPU 311 determines whether i has reached M-1. If it is determined that it has been reached (step S341; Yes), the process is terminated. When it is determined that it has not been reached (step S341; No), i is increased by 1 (step S343), and the process returns to step S311.

FIG. 17A is a flowchart showing the processing for generating the band-specific pitch pulse trains Ppt _{i, j, ω} performed in step S317 of FIG.

The CPU 311 functions as the pitch pulse train generation unit 4 in FIG. 4, so that the pitch pulse train Ppt _{i, j} = {schematically shown in FIG. 17B is based on the pitch length P _{i, j} and the sample selection information. ppt _{i, j, 0} ,..., ppt _{i, j, L-1} } are generated (step S17). FIG. 17B is the same as FIG. 12B already referred to. A method of generating the pitch pulse train Ppt _{i, j} based on the pitch length P _{i, j} and the sample selection information is as already described with reference to FIGS.

  The CPU 311 sets the band identification variable ω to ω = 1 (step S351).

CPU311, by functioning as a band pass filter section 115 as the per-band pitch pulse train generating unit of FIG. 4, the pitch pulse train Ppt _i, the band-by-band pitch pulse train which is a component of the band omega of _{j Ppt i, j, ω =} {ppt _{i, j, ω, 0} ,..., ppt _{i, j, ω, L−1} } are generated (step S353 in FIG. 17).

The CPU 311 determines whether or not ω has reached ω _Max (step S355). If it is determined that it has been reached (step S355; Yes), the process is terminated. If it is determined that it has not been reached (step S355; No), ω is incremented by 1 (step S357), and step S353 is repeated.

FIG. 18A is a flowchart showing the processing for generating the band-specific noise pulse trains Rpt _{i, j, ω} performed in step S319 of FIG.

The CPU 311 functions as the noise pulse train generation unit 211 in FIG. 4, so that a noise pulse train Rpt in which pulses having random numbers of −1 or more and +1 or less are arranged as schematically shown in FIG. 18B. _{i, j} = {rpt _{i, j, 0} ,..., rpt _{i, j, L-1} } are generated (step S18).

  The CPU 311 sets the band identification variable ω to ω = 1 (step S371).

CPU311, by functioning as a band pass filter section 115 as the per-band noise pulse train generating unit of FIG. 4, the noise pulse train Ppt _i, per-band noise pulse train which is a component of the band omega of _{j Rpt i, j, ω =} {rpt _{i, j, ω, 0} ,..., rpt _{i, j, ω, L−1} } are generated (step S373 in FIG. 18).

The CPU 311 determines whether or not ω has reached ω _Max (step S375). If it is determined that it has been reached (step S375; Yes), the process is terminated. If it is determined that it has not been reached (step S375; No), ω is increased by 1 (step S377), and step S373 is repeated.

FIG. 19 shows the band-specific trial excitation signals Ex _{i, j, ω} = {ex _{i, j, ω, 0} ,..., Ex _{i, j, ω, L-1} performed in step S327 of FIG. It is a flowchart which shows the process which produces | generates.

The CPU 311 obtains the trial strength I (0) _{i, j} (step S19). Since this value is corrected later, it is not necessary to obtain an exact value in this step. Of course, it is desirable to adopt a trial value that is as accurate as possible, that is, a value that requires as little correction as possible. In view of such viewpoints, in the present embodiment, as shown by the dotted line arrows in FIGS. 1 and 4, on the basis of the trial the intensity I (0), the coefficient c _{i, j, 0} of zero-order prediction coefficients And
I (0) _{i, j} = exp (c _{i, j, 0} )
And stored in the storage unit 315 (step S391 in FIG. 19).

The CPU 311 checks the storage unit 315 to determine whether or not the pitch length P _{i, j} is stored in the storage unit 315 (step S393 in FIG. 19).

  Basically, as described below, for a band indicated by a flag as having a noisy nature, a trial excitation signal is generated for each band based on the noise pulse train, For the band indicated by the flag as having a pitch-like property, a trial excitation signal for each band of the band is generated based on the pitch pulse train. However, if the entire residual signal does not have a pitch characteristic in the first place, it is determined No in step S313 in FIG. 16 and step S317 is not passed, and therefore no pitch pulse train is generated. Accordingly, as in step S393 in FIG. 19, first, it is determined whether or not the pitch length exists, and if it does not exist, a procedure for generating a trial excitation signal for each band based on the noise pulse train is generated for all bands regardless of the flag. There is a need to.

Therefore, if it is determined that the pitch length P _{i, j} is not stored in the storage unit 315 (step S393; No), the CPU 311 immediately proceeds to step S397 without referring to the value of the flag.

When it is determined that the pitch length P _{i, j} is stored in the storage unit 315 (step S393; Yes), the CPU 311 subsequently determines whether or not the flag Flag _{i, j, ω} = “UV”. (Step S395). If it is determined that the flag Flag _{i, j, ω} = “UV” (step S395; Yes), the process proceeds to step S397. When it is determined that the flag Flag _{i, j, ω} is not “UV” (step S395; No), the process proceeds to step S399.

In step S397, the trial excitation signal for each band Ex _{i, j, ω} is
Ex _{i, j, ω} = I (0) _{i, j} × h _{i, j, ω} × Rpt _{i, j, ω}
Ask for.

In step S399, the band-specific trial excitation signals Ex _{i, j, ω} are
Ex _{i, j, ω} = I (0) _{i, j} × h _{i, j, ω} × Ppt _{i, j, ω}
Ask for.

(Relationship between coefficient vector and inverse quantization coefficient vector)
After that, the pseudo synthesis filter unit 27 of FIG. 1 generates a trial reproduction speech signal, and the correction strength necessary for the speech decoding apparatus 2 of FIG. 2 to generate the decoding excitation signal is determined as the correction factor of FIG. The procedure obtained by the unit 33 will be described. In order to facilitate understanding, first, the relationship between the coefficient vector and the inverse quantization coefficient vector is organized and shown in FIG.

  While the quantized coefficient vector is represented as a table, the inverse quantized vector is a representation of the notation as a concrete vector, but as already explained for vector quantization, here both Are used without distinction. For example, the symbol q is used as a symbol indicating that a certain amount has been quantized, but at the same time, it also indicates the amount that can be dequantized and directly compared with the amount. That is, the quantized result and the amount obtained by dequantizing the result are not distinguished on the notation. This is because, in the present embodiment, the expression mode of the quantization result is not important, but it is important that the information amount reduction by approximation is achieved by quantization.

The table shown in FIG. 20 is a table in which approximation results corresponding to each coefficient vector are added to the list of coefficient vectors for each time series and each dimension already shown in FIG. As a result of the CPU 311 functioning as the vector normalization unit 19, the scalar quantization unit 21, and the vector quantization unit 23 in FIG. 3, in step S <b> 169 and step S <b> 175 in the flowchart in FIG. 8, the storage unit 315 stores the scalar quantization. The coefficient vector maximum absolute value q [Max [c]] _{i, k} and the vector quantization normalized coefficient vector q [n [c]] _{i, k} are stored. As shown in FIG.
V _{i, k} = {c _{i, 0, k} , ..., c _{i, Z-1, k} }
Is the pseudo coefficient vector resulting from the quantization
q [V] _{i, k}
= {q [Max [c]] _{i, k} × q [n [c]] _{i, 0, k} ,..., q [Max [c]] _{i, k} × q [n [c]] _{i, Z-1, k} }
Is approximated by

(Procedure for generating a trial playback audio signal)
In the following, in the speech encoding apparatus 1 according to the present embodiment, the procedure for generating the trial reproduction speech signal from the trial excitation signal performed by the synthesis filter calculation unit 25 and the pseudo synthesis filter unit 27 shown in FIG. This will be described with reference to the flowchart shown in FIG.

As a premise, the trial excitation signal Ex _{i, j} and the scalar quantization coefficient vector maximum absolute value q [Max [c]] _{i, k} and the vector quantization normalization coefficient vector q [shown in the table of FIG. n [c]] _{i and k} are already obtained and stored in the storage unit 315 (0 ≦ i ≦ M−1, 0 ≦ j ≦ Z−1, 0 ≦ k ≦ N-1).

  The CPU 311 sets the main frame counter i to i = 0 (step S21).

The CPU 311 determines the scalar quantization coefficient vector maximum absolute value q [Max [c]] _{i, 0} ,..., Q [Max [c]] _{i, N−1} and the vector quantization normalization coefficient vector q [n. [c]] _{i, 0} ..., q [n [c]] _{i, N−1} are loaded from the storage unit 315 to the register (step S411). Subsequently, the CPU 311 converts the pseudo coefficient vector q [V] _{i, k} into
q [V] _{i, k} = q [Max [c]] _{i, k} × q [n [c]] _{i, k} (0 ≦ k ≦ N-1)
(Step S413).

  The CPU 311 sets the subframe counter j to j = 0 (step S415).

The CPU 311 loads the trial excitation signal Ex _{i, j} from the storage unit 315 to the register (step S417).

CPU311 is pseudo coefficient vector _{q [V] i, 0,} ···, q [V] i, respectively of the j-th component of the _{N-1 q [Max [c} ]] i, 0 × q [n [ c]] _{i, j, 0} ,..., q [Max [c]] _{i, N-1} × q [n [c]] _{i, j, N-1} A pseudo synthesis filter in the subframe identified by j in the middle is defined (step S419). Thereby, the specification of the pseudo synthesis filter unit 27 of FIG. 1 is determined.

The CPU 311 performs a calculation corresponding to passing the trial excitation signal Ex _{i, j} through the pseudo synthesis filter defined in step S419, thereby performing the trial reproduction audio signal W (0) _{i, j} = {w (0 ) _{i, j, 0} ,..., w (0) _{i, j , L-1} } are generated (step S421) and stored in the storage unit 315 (step S423).

  The CPU 311 determines whether j has reached Z-1 (step S425). If it is determined that it has reached (step S425; Yes), the process proceeds to step S429. If it is determined that it has not been reached (step S425; No), j is incremented by 1 (step S427), and the process returns to step S417.

  In step S429, the CPU 311 determines whether i has reached M-1. If it is determined that it has been reached (step S429; Yes), the process is terminated. If it is determined that it has not been reached (step S429; No), i is increased by 1 (step S431), and the process returns to step S411.

(Procedure for generating correction strength)
Hereinafter, in the speech coding apparatus 1 according to the present embodiment, the procedure for generating the correction strength performed by the correction factor determination unit 33 illustrated in FIG. 1 will be described with reference to the flowchart illustrated in FIG.

It is assumed that the input audio signal S _{i, j} is left stored in the storage unit 315, and the trial intensity I (0) _{i, j} and the trial reproduction audio signal W (0) _{i, j} Is already obtained and stored in the storage unit 315. When the pitch length P _{i, j} is obtained in step S217 of FIG. 9 by the pitch extraction unit 113 of FIG. 3, the pitch length P _{i, j} is stored in the storage unit 315 as shown in step S219. (0 ≦ i ≦ M−1, 0 ≦ j ≦ Z−1).

  The CPU 311 sets the main frame counter i to i = 0 (step S22).

  The CPU 311 sets the subframe counter j to j = 0 (step S461).

The CPU 311 searches the storage unit 315 to determine whether or not the pitch length P _{i, j} is stored in the storage unit 315 (step S463).

If it is determined that it is stored (step S463: Yes), the CPU 311 loads the pitch length Pi _{, j} from the storage unit 315 to the register (step S465),
m × P _{i, j} ≦ L-1 <(m + 1) × P _{i, j}
An integer m is obtained, Y = m × P _{i, j} +1 is set (step S467), and the process proceeds to step S471.

  On the other hand, if it is determined that it is not stored (step S463; No), the CPU 311 sets Y = L (step S469), and proceeds to step S471.

In step S471, the CPU 311 determines that the input audio signal S _{i, j} = {s _{i, j, 0} ,..., S _{i, j, L-1} }, the trial reproduction audio signal W (0) _{i, j} = {w (0) _{i, j, 0} ,..., w (0) _{i, j, L-1} } and trial intensity I (0) _{i, j} are loaded from the storage unit 315 to the register. Subsequently, in step S473, the CPU 311 calculates the input audio signal strength sqrt (ΣS _{i, j} ² ) and the trial reproduction audio signal strength sqrt (ΣW (0) _{i, j} ² ),
sqrt (ΣS _{i, j} ² ) = sqrt (s _{i, j, 0} ² + ... + s _{i, j, Y-1} ² )
sqrt (ΣW (0) _{i, j} ² ) = sqrt (w (0) _{i, j, 0} ² + ... + w (0) _{i, j, Y-1} ² )
Calculate as follows.

This calculation is the same as the calculation method of the absolute intensity H _{i, j, ω} by band performed in step S271 and step S273 in FIG. Similarly to the proper use of step S271 and step S273 in FIG. 14, in FIG. 22, the method for determining the Y value corresponding to the integration time length is changed depending on the presence or absence of the pitch length Pi _{, j} . If the pitch length P _{i, j} does not exist, the accuracy is improved by simply performing integration for as long as possible. On the other hand, if the pitch length P _{i, j} exists, simply increasing the integration time length as much as possible. Rather, an integer multiple of the pitch length prevents an error caused by a phase shift.

Subsequently, the CPU 311 determines the correction strength I (1) _{i, j} ,
I (1) _{i, j} = I (0) _{i, j} × {sqrt (ΣS _{i, j} ² ) / sqrt (ΣW (0) _{i, j} ² )}
(Step S475) and stored in the storage unit 315 (Step S477).

  The CPU 311 determines whether j has reached Z-1 (step S479). If it is determined that it has reached (step S479; Yes), the process proceeds to step S483. If it is determined that it has not been reached (step S479; No), j is incremented by 1 (step S481), and the process returns to step S463.

  In step S483, the CPU 311 determines whether i has reached M-1. If it is determined that it has been reached (step S483; Yes), the process is terminated. If it is determined that it has not been reached (step S483; No), i is increased by 1 (step S485), and the process returns to step S461.

(Procedure for generating the excitation signal for decoding from the feature value)
Below, operation | movement of the audio | voice decoding apparatus 2 shown in FIG. 2 is demonstrated. First, a procedure in which the residual signal restoration unit 65 generates a decoding excitation signal will be described.

As a premise, it is assumed that the flag Flag _{i, j, ω} and the band-specific intensities h _{i, j, ω} are decoded by the decoding unit 63 and stored in the storage unit 315. Furthermore, if it exists, it is assumed that the pitch length P _{i, j} and sample selection information are also decoded and stored in the storage unit 315. Further, it is assumed that the correction strengths I (1) _{i, j} are also decoded and stored in the storage unit 315.

  As already described, the residual signal restoration unit 65 shown in FIG. 2 performs an operation very similar to the residual signal feature amount extraction unit 29 shown in FIG. This is because both are common in that a signal for excitation to be input to the synthesis filter is generated from the feature amount. However, the former is for generating a signal for actually restoring the voice, whereas the latter is for obtaining a correction strength which is a correction factor. The correction strength obtained by the latter operation is a given amount in the former operation.

  With the above points in mind, the procedure for generating the decoding excitation signal from the feature quantity is almost the same as the process of generating the trial excitation signal from the feature quantity shown in FIG. That is, in general, the term “trial excitation signal” in FIG. 16 may be read as a decoding excitation signal. Therefore, in order to avoid complication, a flowchart different from FIG. 16 is not shown here.

However, instead of steps S19 and S391 in FIG. 19 in which the details of step S327 in FIG. 16 are shown, a step in which the CPU 311 loads the correction strength I (1) _{i, j} from the storage unit 315 to the register is inserted. In steps S397 and S399, I (1) _{i, j} is used instead of I (0) _{i, j} .

Further, preferably, smoothing processing is performed for each band so that the reproduced signal does not become unnatural due to abrupt change in the residual signal strength for each subframe at the subframe boundary. For this purpose, the procedure for generating the band-specific decoding excitation signals Ex _{i, j, ω,} which is a step corresponding to step S327 of FIG. 16 in generating the decoding excitation signal, is as shown in FIG. .

As shown in the flowchart of FIG. 23, the CPU 311 loads the correction strength I (1) _{i, j} from the storage unit 315 to the register. Further, the CPU 311 determines the correction strength I (1) _pre , the flag Flag _{pre, ω 2} , and the bandwidth in the subframe immediately before the subframe specified by i and j, and the strength of each band. Another strength h _{pre, ω} is loaded from the storage unit 315 to the register (step S23).

The CPU 311 searches the storage unit 315 to determine whether or not the pitch length P _{i, j} is stored in the storage unit 315 (step S511). If it is determined that it is stored (step S511; Yes), the process proceeds to determination step S513. If it is determined that it is not stored (step S511; No), the process proceeds to another determination step S515.

In step S513, the CPU 311 determines whether or not the flags Flag _{i, j, ω} are Flag _{i, j, ω} = “UV”. If it is determined that it is “UV” (step S513; Yes), the process proceeds to determination step S515. If it is determined that it is not “UV” (step S513; No), the process proceeds to another determination step S517.

In step S515, the CPU 311 determines whether or not Flag _{i, j, ω} = Flag _{pre, ω} . If it is determined that Flag _{i, j, ω} = Flag _{pre, ω} (step S515; Yes), the process proceeds to step S551. If it is determined that Flag _{i, j, ω} = Flag _{pre, ω} is not satisfied (step S515; No), the process proceeds to step S553.

Similarly in step S517, the CPU 311 determines whether or not Flag _{i, j, ω} = Flag _{pre, ω} , and if so (step S517; Yes), the process proceeds to step S555. If it is determined that it is not (step S517; No), the process proceeds to step S559.

  Having reached step S551, step S553, step S555, and step S559 through the above steps means that the band-by-band decoding excitation signal in the subframe specified by i and j is the previous subframe, respectively. It means to connect to or switch from noise to noise, non-noise to noise, non-noise to non-noise, and noise to non-noise. After each of these steps, the process ends.

Of these, in the case of switching from non-noise to noise (step S553) and in the case of switching from noise to non-noise (step S559), the above-described smoothing processing is not performed and only steps S397 and S399 in FIG. 19 are performed. Corresponding Ex _{i, j, ω} = I (1) _{i, j} × h _{i, j, ω} × Rpt _{i, j, ω} (step S553) and Ex _{i, j, ω} = I (1) _{i, j} × By calculating h _{i, j, ω} × Ppt _{i, j, ω} (step S559), the band-specific decoding excitation signals Ex _{i, j, ω} are generated.

  Because noise and non-noise are different in nature, no useful information can be obtained by directly comparing the signal strengths. Therefore, when the subframe having noise characteristics and the subframe having no noise characteristics are adjacent to each other in the same band as in the case of step S553 and step S559, the above-described smoothing process is rather performed. If this is not performed, the reproduced audio signal becomes natural.

In steps S551 and S555, band-specific decoding excitation signals Ex _{i, j, and ω} are generated so that the intensity of the band-specific decoding excitation signal changes gently at the subframe boundaries.

  An example of the specific procedure, that is, the smoothing process is shown in the flowchart of FIG. The CPU 311 sets the time series counter t to t = 0 (step S24).

In the case of step S551, that is, when noise subframes are adjacent to each other, ex _{i, j, ω, t,} which are the (t + 1) -th elements of the band-by-band decoding excitation signals Ex _{i, j, ω} . The
ex _{i, j, ω, t}
= {(Lt) × I (1) _pre × h _{pre, ω} + t × I (1) _{i, j} × h _{i, j, ω} } × rpt _{i, j, ω, t} / L
Ask for.

If in step S555, i.e., when the non-noise sub-frame are adjacent to each other, the bandwidth for a different decoded excitation signal Ex _{i, j,} a second (t + 1) th element of _omega ex _{i, j, omega, t}
ex _{i, j, ω, t}
= {(Lt) × I (1) _pre × h _{pre, ω} + t × I (1) _{i, j} × h _{i, j, ω} } × ppt _{i, j, ω, t} / L
(Step S561).

  The CPU 311 determines whether or not t has reached L−1 (step S563). If it is determined that it has been reached (step S563; Yes), the process is terminated. If it is determined that it has not been reached (step S563; No), t is increased by 1 (step S565), and then step S561 is repeated.

  The band-specific decoding excitation signals obtained as described above are summed over all the bands, and are output from the residual signal restoration unit 65 of FIG. 2 as decoding excitation signals.

(Procedure for generating audio playback signal)
The signal generated as described above by the residual signal restoration unit 65 of FIG. 2 and output as the excitation signal for decoding is passed through the pseudo synthesis filter unit 27 to be converted into a reproduced audio signal. The specific procedure is based on FIG. The trial reproduction audio signal is replaced with a simple reproduction audio signal, the trial excitation signal is replaced with a decoding excitation signal, and the like.

For example, the pseudo synthesis filter unit 27 in FIG. 4 is defined by the synthesis filter calculation unit 25 by the values of the components of the pseudo coefficient vectors q [V] _{i and k} shown in the table of FIG. 20 (step of FIG. 21). Equivalent to S419).

(Embodiment 2)
The speech encoding apparatus 1 according to the first embodiment identifies a reference residual sample, and from among the samples existing in a section within one pitch length backward in time series from the reference residual sample, that is, in a search target section, Select several samples with large absolute values, and the relative size of these samples with respect to the reference residual sample and the relative position on the time series with respect to the reference residual sample are used as sample selection information. .

  The speech encoding apparatus according to the present embodiment also determines the search target section in the same manner as the speech encoding apparatus 1 according to the first embodiment.

  However, the speech encoding apparatus according to the present embodiment equally divides the search target section into a predetermined number of small sections in advance, specifies a sample having the maximum absolute value for each small section, and The relative size with respect to the reference residual sample is used as sample selection information.

  In this embodiment, the number of small sections included in the search target section is determined in advance between the speech coding apparatus and the speech decoding apparatus.

  Furthermore, between the speech coding apparatus and the speech decoding apparatus, it is agreed that the sample selection information is transmitted from the former to the latter, for example, in chronological order.

  Then, the speech decoding apparatus that receives the sample selection information in this way assigns the sample received as the first sample to the first subsection. Subsequently, the speech decoding apparatus assigns the sample received as the second sample to the second small section. The same applies hereinafter. In this manner, the speech decoding apparatus according to the present embodiment generates a pitch pulse train for generating a decoding excitation signal.

  It is assumed that the speech decoding apparatus assigns the received sample to a predetermined time with reference to the leading sampling time in the small section.

  For this reason, unlike the case of the first embodiment, in the case where two samples having relatively large absolute values happen to belong to the same small section on the speech encoding device side, one sample is Despite its saliency, it is not selected. Therefore, as a matter of course, the speech decoding apparatus cannot reproduce such a sample that has been discarded. The other is that the speech decoding apparatus is not informed of the exact sampling time at which the sample should be present, but only in which subsection the sample belongs. Therefore, the position of the selected sample in time series is shifted by the maximum time length of a small section in the process of information transmission from the speech coding apparatus to the speech decoding apparatus.

  That is, when compared under the condition that the same number of samples are transmitted and received, it can be said that the speech decoding apparatus according to the present embodiment does not generate a pitch pulse train as accurately as the speech decoding apparatus 2 according to the first embodiment.

  However, in the case of the present example, unlike the case of the first embodiment, the speech encoding device does not need to notify the speech decoding device of the relative position of the selected sample with respect to the reference residual sample.

  Therefore, when sample selection information is generated for the same number of samples as in the first embodiment, the information is small, which is advantageous when the communication bit rate is limited.

  Hereinafter, the method of selecting a sample and the method of generating a pitch pulse train on the speech decoding apparatus side in the present embodiment described so far will be described with reference to specific examples shown in FIGS. To do.

  The residual signal is assumed to be as shown in FIG. This is the same as FIG. 11A referred to when the first embodiment is described. Therefore, the features of this embodiment compared to Embodiment 1 can be easily understood by comparing FIGS. 25 and 26 with FIGS.

As shown in FIG. 25B, the time interval corresponding to the pitch length P _{i, j} is assumed to be divided into four small intervals here. Then, as shown in FIG. 25C, the sample having the maximum absolute value in each small section is selected.

  The selected specimen is different from FIG. 11C in which the same number of specimens are selected. This is because the sample selection method is different from the case of the first embodiment. For example, the sample selected as u (2) in FIG. 11 (c) has a relatively significant size, but is not selected in FIG. 25 (c). This is because in FIG. 25C, only the larger sample that happens to be included in the same small section as the sample is selected as u (1).

  Here, it is assumed that the selected sample is predetermined on the speech decoding apparatus side to be assigned to the first sampling time of the small section. Then, in the case of this embodiment, the signal which becomes the repetition unit of the pitch pulse train on the speech decoding apparatus side is as shown in FIG. Compared to FIG. 25 (c), the position of the sample is shifted. This is because the speech decoding apparatus according to the present embodiment grasps the position of the sample in time series only in units of small sections. In this regard, in the case of the first embodiment, as shown in FIGS. 11C and 12A, the position of the specimen does not shift.

  The pitch pulse train generated by the speech decoding apparatus according to this embodiment is as shown in FIG. This is different from the case of the first embodiment shown in FIG. FIG. 26C shows the original residual signal, which is the same as FIGS. 25A, 11A, and 12C.

(Embodiment 3)
In the second embodiment, the sample having the maximum absolute value is selected from the small sections. On the other hand, in this embodiment, a value obtained by dividing the average value of all samples included in the small section by the absolute value of the reference residual sample is used as sample selection information.

  That is, in the case of the second embodiment, the specific small sample included in the small section is used to represent the small section. In the case of the present embodiment, the calculation is performed based on all the samples included in the small section. The small section is represented by the obtained value.

  The features of this embodiment compared to the first embodiment are the same as those of the second embodiment.

  In addition, this invention is not limited to the said embodiment, A various deformation | transformation and application are possible. The above-described hardware configuration, block configuration, and flowchart are merely examples for explanation, and are not examples for limiting the scope of the present invention.

  For example, a mobile phone has been cited as an example of the speech encoding / decoding device 3 shown in FIG. 5. Similarly, the present invention can be applied. For example, when the present invention is applied to a personal computer, if a voice input / output device, a communication device, or the like is added to the personal computer, it can have the function of a mobile phone as hardware. Then, if a computer program for causing a computer to execute the above-described processing is distributed by a recording medium or communication, the computer is installed and executed on the computer, thereby causing the computer to execute the speech encoding apparatus or the speech according to the present invention. It is also possible to function as a decoding device.

  That is, the said embodiment is for description and does not restrict | limit the scope of the present invention. Therefore, those skilled in the art can employ embodiments in which each or all of these elements are replaced with equivalent ones, and these embodiments are also included in the scope of the present invention.

It is a figure which shows the function structure of the audio | voice coding apparatus which concerns on Embodiment 1 of this invention. It is a figure which shows the function structure of the speech decoding apparatus which concerns on Embodiment 1 of this invention. It is a figure which shows the detailed functional structure of a residual signal feature-value extraction part. It is a figure which shows the detailed functional structure of a residual signal restoration trial part. It is a figure which shows the physical structure of the audio | voice encoding and decoding apparatus which concerns on Embodiment 1 of this invention. It is a figure which shows typically a mode that a coefficient vector comprised from a prediction coefficient is shown as a table | surface, and a coefficient vector is quantized. It is a figure which shows the flow of a prediction analysis. It is a figure which shows the flow which performs vector normalization, scalar quantization, and vector quantization. It is a figure which shows the flow which produces | generates pitch length, sample selection information, a flag, and the intensity | strength according to a band from a residual signal. It is a figure which shows the flow which produces | generates sample selection information. It is a figure which shows the first half of the specific example of sample selection and pitch pulse train generation in Embodiment 1 of this invention. It is a figure which shows the second half of the specific example of the sample selection and pitch pulse train generation in Embodiment 1 of this invention. It is a figure which shows the flow which produces | generates the flag about band (omega). It is a figure which shows the flow which calculates | requires the absolute strength according to zone | band of the residual signal according to zone | band. It is a figure which shows the flow which produces | generates the intensity | strength according to zone | band. It is a figure which shows the flow which produces | generates the excitation signal for trial from pitch length, sample selection information, a flag, and the intensity | strength according to a zone | band. It is a figure which shows the flow which produces | generates the pitch pulse train according to zone | band, and shows a pitch pulse train typically. It is a figure which shows the flow which produces | generates the noise pulse train according to zone | band, and shows a noise pulse train typically. It is a figure which shows the flow which produces | generates the excitation signal for trial according to band. It is the figure which showed the coefficient vector and the pseudo coefficient vector side by side as a table. It is a figure which shows the flow which produces | generates the reproduction audio | voice signal for trial. It is a figure which shows the flow which produces | generates correction intensity | strength. It is a figure which shows the flow which produces | generates the excitation signal for decoding according to zone | bands. It is a figure which shows the flow which produces | generates the excitation signal for decoding according to a band so that an intensity | strength may change smoothly in the boundary of a sub-frame. It is a figure which shows the first half of the specific example of the sample selection and pitch pulse train generation in Embodiment 2 of this invention. It is a figure which shows the second half of the specific example of the sample selection and pitch pulse train production | generation in Embodiment 2 of this invention.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 1 ... Speech coding apparatus according to Embodiment 1, 2 ... Speech decoding apparatus according to Embodiment 1, 3 ... Total pitch discrimination unit, 4 ... Pitch pulse train generation unit, 5 ... Implementation Speech encoding and decoding apparatus according to embodiment 1, 11 ... microphone, 13 ... A / D conversion unit, 15 ... prediction analysis unit, 17 ... inverse filter calculator for prediction analysis, 19 ... -Vector normalization unit, 21 ... scalar quantization unit, 23 ... vector quantization unit, 25 ... synthesis filter calculation unit, 27 ... pseudo synthesis filter unit, 29 ... residual signal feature Quantity extraction unit 31 ... Residual signal restoration trial unit 33 ... Correction factor determination unit 35 ... Coding unit 37 ... Transmission unit 61 ... Reception unit 63 ... Decoding unit, 65... Residual signal restoration unit, 67... D / A conversion unit, 69. DESCRIPTION OF SYMBOLS 11 ... Switch for pitch extraction, 113 ... Pitch extraction part, 115 ... Band filter part, 117 ... 1st band filter, 119 ... 2nd band filter, 121 ... 3rd band Filter, 123... Noise discrimination unit for each band, 125... First band noise discriminator, 127... Second band noise discriminator, 129. , 133... 1st band intensity calculator, 135... 2nd band intensity calculator, 141... Sample selection section, 211... Noise pulse train generator, 221. Signal generation unit, 223 ... Trial excitation signal generator for each first band, 225 ... Trial excitation signal generator for each second band, 227 ... Trial excitation signal generation unit, 311 ... CPU 313 ... ROM, 315 ... memory 317: Wireless communication unit, 319 ... Audio processing unit, 321 ... Operation key input processing unit, 323 ... System bus, 325 ... Antenna, 327 ... Operation key, 329 ...・ RAM, 331 ... Hard disk

Claims (3)

A prediction analysis unit that decomposes a speech signal into a prediction signal and a residual signal that is a time series of residual samples;
A pitch length extraction unit for extracting a pitch length from the residual signal;
A reference residual sample that is a residual sample having the largest absolute value among the residual samples is identified, a residual sample maximum absolute value that is an absolute value of the reference residual sample is obtained, and the reference residual sample is obtained. A time zone corresponding to one pitch length starting from the corresponding time is divided into a predetermined number of segment time zones, and an average value of residual samples is determined by the residual sample maximum absolute value for each segment time zone. A feature amount extraction unit for obtaining a residual sample strength ratio by segment time, which is a value obtained by dividing,
An encoding unit that encodes the prediction coefficient, the pitch length, and the segmented time residual sample intensity ratio;
A speech encoding device comprising: A predictive analysis step that decomposes the speech signal into a prediction signal and a residual signal that is a time series of residual samples;
A pitch length extraction step for extracting a pitch length from the residual signal;
A reference residual sample that is a residual sample having the largest absolute value among the residual samples is identified, a residual sample maximum absolute value that is an absolute value of the reference residual sample is obtained, and the reference residual sample is obtained. A time zone corresponding to one pitch length starting from the corresponding time is divided into a predetermined number of segment time zones, and an average value of residual samples is determined by the residual sample maximum absolute value for each segment time zone. A feature amount extraction step for obtaining a residual sample strength ratio by segment time, which is a value obtained by dividing,
An encoding step for encoding the prediction coefficient, the pitch length, and the residual sample strength ratio by segment time;
A speech encoding method comprising: On the computer,
A predictive analysis step that decomposes the speech signal into a prediction signal and a residual signal that is a time series of residual samples;
A pitch length extraction step for extracting a pitch length from the residual signal;
A reference residual sample that is a residual sample having the largest absolute value among the residual samples is identified, a residual sample maximum absolute value that is an absolute value of the reference residual sample is obtained, and the reference residual sample is obtained. A time zone corresponding to one pitch length starting from the corresponding time is divided into a predetermined number of segment time zones, and an average value of residual samples is determined by the residual sample maximum absolute value for each segment time zone. A feature amount extraction step for obtaining a residual sample strength ratio by segment time, which is a value obtained by dividing,
An encoding step for encoding the prediction coefficient, the pitch length, and the residual sample strength ratio by segment time;
A program for running

Images