Voice encoding method

Details Voice encoding method Voice encoding method

1999-08-11

2002-04-02

Korzuch, William (Department: 2641)

Data processing: speech signal processing, linguistics, language

Speech signal processing

For storage or transmission

C704S219000

Reexamination Certificate

active

06366881

ABSTRACT:

TECHNICAL FIELD
The present invention relates generally to a voice coding method, and more particularly, to improvements of an adaptive pulse code modulation (APCM) method and an adaptive differential pulse code modulation (ADPCM) method.
BACKGROUND
As a coding system of a voice signal, an adaptive pulse code modulation (APCM) method and an adaptive difference pulse code modulation (ADPCM) method, and so on have been known.
The ADPCM is a method of predicting the current input signal from the past input signal, quantizing a difference between its predicted value and the current input signal, and then coding the quantized difference. On the other hand, in the ADPCM, a quantization step size is changed depending on the variation in the level of the input signal.
FIG. 11
illustrates the schematic construction of a conventional ADPCM encoder 4 and a conventional ADPCM decoder
5
. n used in the following description is an integer.
Description is now made of the ADPCM encoder
4
.
A first adder
41
finds a difference (a prediction error signal d
n
) between a signal x
n
signal y
n
on the basis of the following equation (1):
d
n
=x
n
−y
n
  (1)
A first adaptive quantizer
42
codes the prediction error signal d
n
found by the first adder
41
on the basis of a quantization step size T
n
, to find a code L
n
. That is, the first adaptive quantizer
42
finds the code L
n
on the basis of the following equation (2). The found code L
n
is sent to a memory
6
.
L
n
=[d
n
/T
n
]  (2)
In the equation (2), [ ] is Gauss' notation, and represents the maximum integer which does not exceed a number in the square brackets. An initial value of the quantized value T
n
is a positive number.
A first quantization step size updating device
43
finds a quantization step size T
n+1
corresponding the subsequent voice signal sampling value X
n+1
on the basis of the following equation (3). The relationship between the code L
n
and a function M (L
n
) is as shown in Table 1. Table 1 shows an example in a case where the code L
n
is composed of four bits.
 T
n+1
=T
n
×M(L
n
)  (3)
TABLE 1
L
n
M (L
n
)
0
−1
0.9
1
−2
0.9
2
−3
0.9
3
−4
0.9
4
−5
1.2
5
−6
1.6
6
−7
2.0
7
−8
2.4
A first adaptive reverse quantizer
44
reversely quantizes the prediction error signal d
n
using the code L
n
, to find a reversely quantized value q
n
. That is, the first adaptive reverse quantizer
44
finds the reversely quantized value q
n
on the basis of the following equation (4):
q
n
=(L
n
+0.5)×T
n
  (4)
A second adder
45
finds a reproducing signal w
n
the basis of the predicting signal y
n
ponding to the current voice signal sampling x
n
and the reversely quantized value q
n
. That is, the second adder
45
finds the reproducing signal w
n
on the basis of the following equation (5):
w
n
=y
n
+q
n
  (5)
A first predicting device
46
delays the reproducing signal w
n
by one sampling time, to find a predicting signal y
n+1
corresponding to the subsequent voice signal sampling value x
+1
.
Description is now made of the ADPCM decoder
5
.
A second adaptive reverse quantizer
51
uses a code L
n
′ obtained from the memory
6
and a quantization step size T
n
′ obtained by a second quantization step size updating device
52
, to find a reversely quantized value q
n
′ on the basis of the following equation (6).
q
n
′=(L
n
′+0.5)×T
n
′  (6)
If L
n
found in the ADPCM encoder
4
is correctly transmitted to the ADPCM decoder
5
, that is, L
n
=L
n
′, the values of q
n
′, y
n
′, T
n
′ and w
n
′ used on the side of the ADPCM decoder
5
are respectively equal to the values of q
n
, y
n
, T
n
and w
n
used on the side of the ADPCM encoder
4
.
The second quantization step size updating device
52
uses the code L
n
′ obtained from the memory
6
, to find a quantization step size T
n+1
′ used with respect to the subsequent code L
n+1
′ on the basis of the following equation (7) The relationship between L
n
′ and a function M (L
n
′) in the following equation (7) is the same as the relationship between L
n
and the function M (L
n
) in the foregoing Table 1.
T
n+1
′=T
n
′×M(L
n
′)  (7)
A third adder
53
finds a reproducing signal w
n
′ on the basis of a predicting signal y
n
′ obtained by a second predicting device
54
and the reversely quantized value q
n
′. That is, the third adder
53
finds the reproducing signal w
n
′ on the basis of the following equation (8). The found reproducing signal w
n
′ is outputted from the ADPCM decoder
5
.
w
n
′=y
n
′+q
n
′  (8)
The second predicting device
54
delays the reproducing signal w
n
′ by one sampling time, to find the subsequent predicting signal y
n+1
′, and sends the predicting signal y
n+1
′ to the third adder
53
.
FIGS. 12 and 13
illustrate the relationship between the reversely quantized value q
n
and the prediction error signal d
n
in a case where the code L
n
is composed of three bits.
T in
FIG. 12 and U
in
FIG. 13
respectively represent quantization step sizes determined by the first quantization step size updating device
43
at different time points, where it is assumed that T<U.
In a case where the range A to B of the prediction error signal d
n
is indicated by A and B, the range is indicated by “[A” when a boundary A is included in the range, while being indicated by “(A” when it is not included therein. Similarly, the range is indicated by “B]” when a boundary B is included in the range, while being indicated by “B)” when it is not included therein.
In
FIG. 12
, the reversely quantized value q
n
is 0.5T when the value of the prediction error signal d
n
is in the range of [0, T), 1.5T when it is in the range of [T, 2T), 2.5T when it is in the range of [2T, 3T) and 3.5T when it is in the range of [3T, ∞].
The reversely quantized value q
n
is −0.5T when the value of the prediction error signal d
n
is in the range of [−T, 0), −1.5T when it is in the range of [−2T, −T) −
2
.
5
when it is in the range of [−3T, −2T), and −3.5T when it is in the range of [−∞, −3T)
In the relationship between the reversely quantized value q
n
and the prediction error signal d
n
in
FIG. 13
, T in
FIG. 12
is replaced with U. As shown in
FIGS. 12 and 13
, the relationship between the reversely quantized value q
n
and the prediction error signal d
n
is so determined that the characteristics are symmetrical in a positive range and a negative range of the prediction error signal d
n
in the prior art. As a result, even when the prediction error signal d
n
is small, the reversely quantized value q
n
is not zero.
As can be seen from the equation (3) and Table 1, when the code L
n
becomes large, the quantization step size T
n
is made large. That is, the quantization step size is made small as shown in
FIG. 12
when the prediction error signal d
n
is small, while being made large as shown in
FIG. 13
when the prediction error signal d
n
is large.
In a voice signal, there exist a lot of silent sections where the prediction error signal d
n
is zero. In the above-mentioned prior art, however, even when the prediction error signal d
n
is zero, the reversely quantized value q
n
is 0.5T(or 0.5U) which is not zero, so that an quantizing error is increased.
In the above-mentioned prior art, even if the absolute value of the prediction error signal d
n
is rapidly changed from a large value to a small value, a large value corresponding to the previous prediction error signal d
n
whose absolute value is large is maintained as the quantization

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