Sound encoder and sound decoder

Details Sound encoder and sound decoder Sound encoder and sound decoder

1999-06-18

2002-07-02

Dorvil, Richemond (Department: 2641)

Data processing: speech signal processing, linguistics, language

Speech signal processing

For storage or transmission

C704S222000, C704S219000

Reexamination Certificate

active

06415254

ABSTRACT:

TECHNICAL FIELD
The present invention relates to a speech coder for efficiently coding speech information and a speech decoder for efficiently decoding the same.
BACKGROUND ART
A speech coding technique for efficiently coding and decoding speech information has been developed in recent years. In Code Excited Linear Prediction: “High Quality Speech at Low Bit Rate”, M. R. Schroeder, Proc. ICASSP '85, pp. 937-940, there is described a speech coder of a CELP type, which is on the basis of such a speech coding technique.
In this speech coder, a linear prediction for an input speech is carried out in every frame, which is divided at a fixed time. A prediction residual (excitation signal) is obtained by the linear prediction for each frame. Then, the prediction residual is coded using an adaptive codebook in which a previous excitation signal is stored and a random codebook in which a plurality of random code vectors is stored.
FIG. 1
shows a functional block of a conventional CELP type speech coder.
A speech signal
11
input to the CELP type speech coder is subjected to a linear prediction analysis in a linear prediction analyzing section
12
. A linear predictive coefficients can be obtained by the linear prediction analysis. The linear predictive coefficients are parameters indicating an spectrum envelop of the speech signal
11
. The linear predictive coefficients obtained in the linear prediction analyzing section
12
are quantized by a linear predictive coefficient coding section
13
, and the quantized linear predictive coefficients are sent to a linear predictive coefficient decoding section
14
. Note that an index obtained by this quantization is output to a code outputting section
24
as a linear predictive code. The linear predictive coefficient decoding section
14
decodes the linear predictive coefficients quantized by the linear predictive coefficient coding section
13
so as to obtain coefficients of a synthetic filter. The linear predictive coefficient decoding section
14
outputs these coefficients to a synthetic filter
15
.
An adaptive codebook
17
is one, which outputs a plurality of candidates of adaptive codevectors, and which comprises a buffer for storing excitation signals corresponding to previous several frames. The adaptive codevectors are time series vectors, which express periodic components in the input speech.
A random codebook
18
is one, which stores a plurality of candidates of random codevectors. The random code vectors are time series vectors, which express non-periodic components in the input speech.
In an adaptive code gain weighting section
19
and a random code gain weighting section
20
, the candidate vectors output from the adaptive codebook
17
and the random codebook
18
are multiplied by an adaptive code gain read from a weight codebook
21
and a random code gain, respectively, and the resultants are output to an adding section
22
.
The weighting codebook stores a plurality of adaptive codebook gains by which the adaptive codevector is multiplied and a plurality of random codebook gains by which the random codevectors are multiplied.
The adding section
22
adds the adaptive code vector candidates and the random code vector candidates, which are weighted in the adaptive code gain weighting section
19
and the random code gain weighting section
20
, respectively. Then, the adding section
22
generates excitation vectors so as to be output to the synthetic filter
15
.
The synthetic filter
15
is an all-pole filter. The coefficients of the synthetic filter are obtained by the linear predictive coefficient decoding section
14
. The synthetic filter
15
has a function of synthesizing input excitation vector in order to produce synthetic speech and outputting that synthetic speech to a distortion calculator
16
.
A distortion calculator
16
calculates a distortion between the synthetic speech, which is the output of the synthetic filter
15
, and the input speech
11
, and outputs the obtained distortion value to a code index specifying section
23
. The code index specifying section
23
specifies three kinds of codebook indicies (index of adaptive codebook, index of random codebook, index of weight codebook) so as to minimize the distortion calculated by the distortion calculation section
16
. The three kinds of codebook indicies specified by the code index specifying section
23
are output to a code outputting section
24
. The code outputting section
24
outputs the index of linear predictive codebook obtained by the linear predictive coefficient coding section
13
and the index of adaptive codebook, the index of random code, the index of weight codebook, which have been specified by the code index specifying section
23
, to a transmission path at one time.
FIG. 2
shows a functional block of a CELP speech decoder, which decodes the speech signal coded by the aforementioned coder. In this speech decoder apparatus, a code input section
31
receives codes sent from the speech coder (FIG.
1
). The received codes are disassembled into the index of the linear predictive codebook, the index of adaptive codebook, the index of random codebook, and the index of weight codebook. Then, the indicies obtained by the above disassemble are output to a linear predictive coefficient decoding section
32
, an adaptive codebook
33
, a random codebook
34
, and a weight codebook
35
, respectively.
Next, the linear predictive coefficient decoding section
32
decodes the linear predictive code number obtained by the code input section
31
so as to obtain coefficients of the synthetic filter, and outputs those coefficients to a synthetic filter
39
. Then, an adaptive codevector corresponding to the index of adaptive codebook is read from adaptive codebook, and a random codevector corresponding to the index of random codebook is read from the random codebook. Moreover, an adaptive codebook gain and a-random codebook gain corresponding to the index of weight codebook are read from the weight codebook. Then, in an adaptive codevector weighting section
36
, the adaptive codevector is multiplied by the adaptive codebook gain, and the resultant is sent to an adding section
38
. Similarly, in a random codevector weighting section
37
, the random codevector is multiplied by the random codebook gain,: and the resultant is sent to the adding section
38
.
The adding section
38
adds the above two codevectors and generates an excitation vector. Then, the generated excitation vector is sent to the adaptive codebook
33
to update the buffer or the synthetic filter
39
to excite the filter. The synthetic filter
39
, composed with the linear predictive coeffcients which are output from linear predictive coefficient decoding section
32
, is excited by the excitation vector obtained by the adding section
38
, and reproduces a synthetic speech.
Note that, in the distortion calculator
16
of the CELP speech coder, distortion E is generally calculated by the following expression (1):
 
E=∥v
−(
gaHP+gcHC
)∥
2
  (1)
where v: an input speech signal (vector),
H: an impulse response convolution matrix for a synthetic filter
H
=
[
h
⁡
(
0
)
0
⋯
⋯
0
0
h
⁡
(
1
)
h
⁡
(
0
)
0
⋯
0
0
h
⁡
(
2
)
h
⁡
(
1
)
h
⁡
(
0
)
0
0
0
⋮
⋮
⋮
⋰
0
0
⋮
⋮
⋮
⋰
h
⁡
(
0
)
0
h
⁡
(
L
-
1
)
⋯
⋯
⋯
h
⁡
(
1
)
h
⁡
(
0
)
]
wherein h is an impulse response of a synthetic filter, L is a frame length,
p: an adaptive codevector,
c: a random codevector,
ga: an adaptive codebook gain
gc: a random codebook gain
Here, in order to minimize distortion E of expression (1), the distortion is calculated by a closed loop with respective to all combinations of the adaptive code number, the random code number, the weight code number, it is necessary to specify each code number.
However, if the closed loop search is performed with respect to expression (1), an amount of calculation processing becomes too large. For this reason, gen

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