Data processing: speech signal processing – linguistics – language – Speech signal processing – For storage or transmission
Reexamination Certificate
1999-03-04
2003-12-02
Knepper, David D. (Department: 2654)
Data processing: speech signal processing, linguistics, language
Speech signal processing
For storage or transmission
C704S500000, C348S398100
Reexamination Certificate
active
06658379
ABSTRACT:
TECHNICAL FIELD
This invention relates to a data processing method and a data processing device for carrying out data processing by dividing data of a finite length into a plurality of frequency bands, which are applied to an AV equipment, a communication equipment and a database device for carrying out coding for the purpose of audio and video data compression or decoding thereof.
BACKGROUND ART
As a coding/decoding method for the purpose of compressing digital signals, a subband coding is employed. This subband coding is adapted for carrying out band division of digital signals by using a filter for carrying out wavelet transform (hereinafter referred to as a wavelet transform filter) so as to compress the digital signals. Specifically, subband coding is adapted for carrying out filtering processing on input signals by using a plurality of filters having different passbands and then carrying out down-sampling at an interval corresponding to each frequency band, so as to carry out compression utilizing the bias of energy of an output signal from each filter.
Signal processing by band division utilizing subband coding and wavelet transform is described in, for example, Martin Vetari, “Wavelet Transform and Subband Coding”, Electronic Telecommunication Society, Vol. 1.74, No. 12, pp.1275-1278, December 1991.
In general, wavelet transform is defined as a narrower term or improvement of subband coding. However, the following description of wavelet includes a technique using not only a wavelet transform filter but also a filter applied to subband coding.
FIG. 1
shows the basic structure for band division and synthesis by wavelet transform and inverse wavelet transform. In
FIG. 1
, a one-dimensional signal x[i] is used as an input.
A wavelet transform unit
100
shown in
FIG. 1
divides an input signal x[i] into signals xa′[j], xb′[j], xc′[j] . . . of plural frequency bands (subbands). An inverse wavelet transform unit
200
synthesizes the signals xa′[j], xb′[j], xc′[j] . . . divided into subbands so as to restore an input signal x″[i]. A signal processor
300
carries out predetermined processing on data which has been divided into the frequency bands. For example, in the case where coding processing is to be carried out, quantization, variable length coding, transmission, variable length decoding, and inverse quantization are carried out.
Specifically, in the wavelet transform unit
100
, analysis filters
111
,
112
,
113
. . . carry out filtering for band division. Then, down-sampling units
121
,
122
,
123
. . . carry out down-sampling for storing data at a given sampling interval Di (i=1, 2, 3 . . . ) while thinning the other data with respect to data arrays xa[j], xb[j], xc[j] . . . of the individual frequency bands filtered and divided by the analysis filters
111
,
112
,
113
. . . , thereby generating the signals xa′[j], xb′[j], xc′[j] . . . of the individual frequency bands.
On the other hand, in the inverse wavelet transform unit
200
, up-sampling units
211
,
212
,
213
. . . insert an appropriate number of zeros between two adjacent data with respect to the signals xa′[j], xb′[j], xc′[j] . . . of the individual frequency bands inputted thereto. The number of zeros to be inserted is equal to the number of samples (Di−1) of the data thinned by the corresponding down-sampling units
121
,
122
,
123
. . . Then, synthesis filters
221
,
222
,
223
. . . carry out filtering for interpolation with respect to data arrays xa″[j], xb″[j], xc″[j] . . . in which zero values are inserted. An adder
230
adds the data arrays xa″[j], xb″[j], xc″[j] . . . of the individual frequency bands interpolated by the synthesis filters
221
,
222
,
223
. . . so as to restore the input signal x[i] as the synthesis output signal x″[i].
An example where input data is divided into two subbands will now be described in detail. In this case, the two analysis filters
111
,
112
in the wavelet transform unit
100
become a low-pass filter and a high-pass filter, respectively. These analysis low-pass filter
111
and analysis high-pass filter
112
divide the input signal x[i] into a low-frequency band signal XL[i] and a high-frequency band signal XH[i]. The down-sampling units
121
,
122
carry out thinning for every sample with respect to each of the divided signals, as expressed by the following Equations (1) and (2).
XL[j]=XL[i],j=i
/2 Equation (1)
XH[j]=XH[i],j=i
/2 Equation (2)
In the inverse wavelet transform unit
200
, first, the up-sampling units
211
,
212
extend the sampling interval twice, and a sample having a zero value at the center position is inserted, as expressed by the following Equations (3) and (4).
XL[i]=XL[j]. . . i
=2
×j
0
. . . i=
2
×j
1 Equation (3)
XH[i]=XH[j] . . . i
=2
×j
0
. . . i
=2
×j
+1 Equation (4)
Then, the signals XL[i], XH[i] of the individual frequency bands obtained on up-sampling by the up-sampling units
211
,
212
are supplied to the adder
230
through the synthesis low-pass filter
221
and the synthesis high-pass filter
222
corresponding to the analysis low-pass filter
111
and the analysis high-pass filter
112
, respectively. The synthesis low-pass filter
221
and the synthesis high-pass filter
222
carry out interpolation on the output signals XL[i], XH[i] of the up-sampling units
211
,
212
. After that, the adder
230
adds the signals XL[i], XH[i] of the individual frequency bands, thereby restoring the input signal x[i] as the synthesis output signal x″[i].
The analysis low-pass filter
111
and the analysis high-pass filter
112
used in the wavelet transform unit
100
, and the synthesis low-pass filter
221
and the synthesis high-pass filter
222
used in the inverse wavelet transform unit
200
, are constituted to completely or proximately satisfy the relations of the following Equations (5) and (6).
H
0
(−
z
)
F
0
(
z
)+
H
1
(−
z
)
F
1
(
z
)=0 Equation (5)
H
0
(
z
)
F
0
(
z
)+
H
1
(
z
)
F
1
(
z
)=2
z
−L
Equation (6)
In Equation s (5) and (6), H
0
(z), H
1
(z), F
0
(z) and F
1
(z) represent transfer functions of the analysis low-pass filter
111
, the analysis high-pass filter
112
, the synthesis low-pass filter
221
and the synthesis high-pass filter
222
, respectively, and L is an arbitrary integer. Under this constraint, if input data has an infinite length, it is ensured that the synthesis output signal x″[i] from the adder
230
in the inverse wavelet transform unit
200
completely or proximately coincides with the input signal x[i].
Exemplary filter coefficients of the analysis low-pass filter
111
and the analysis high-pass filter
112
and filter coefficients of the corresponding synthesis low-pass filter
221
and synthesis high-pass filter
222
are shown in the following Table 1.
TABLE 1
Coefficient of Wavelet Filter
Analysis Filter Coefficient
Synthesis Filter Coefficient
Low-Pass Filter
High-Pass Filter
Low-Pass Filter
High-Pass Filter
[0]
0.046875
[0]
0.500000
[0]
0.250000
[0]
−0.023438
[1]
−0.093750
[1]
−1.000000
[1]
0.500000
[1]
−0.046875
[2]
−0.250000
[2]
0.500000
[2]
0.250000
[2]
0.125000
[3]
0.593750
[3]
0.2968
Frommer William S.
Frommer & Lawrence & Haug LLP
Knepper David D.
Simon Darren M.
Sony Corporation
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