Nippon Telegraph and Telephone Corporation

Satoshi Miki

Takehiro Moriya

Kazunori Mano

Naoki Iwakami

Shinji Hayashi

Hirohito Suda

Shigeaki Sasaki

Hitoshi Ohmuro

Akitoshi Kataoka

Hitoshi Omuro

Hitoshi Ohmura

In a speech coding method of the present invention, initially, a plurality of samples of speech data are analyzed by a linear prediction analysis and thereby prediction coefficients are calculated. Then, the prediction coefficients are quantized, and the quantized prediction coefficients are set in a synthesis filter. Moreover, a pitch period vector is selected from an adaptive codebook in which a plurality of pitch period vectors are stored, and the selected pitch period vector is multiplied by a first gain which is obtained, at the same time, with a second gain. In addition, a noise waveform vector is selected from a random codebook in which a plurality of the noise waveform vectors are stored, and is multiplied by a predicted gain and the second gain. Then, the speech vector is synthesized by exciting the synthesis filter with the pitch period vector multiplied by the first gain, and with the noise waveform vector multiplied by the predicted gain and the second gain. Consequently, speech data comprising a plurality of samples are coded as a unit of a frame operation. Furthermore, the predicted gain multiplied by the noise waveform vector which is selected in a subsequent frame operation, is predicted based on the current noise waveform vector which is multiplied by the predicted gain and the second gain at the current frame operation, and also the previous waveform vector which is multiplied by the predicted gain and the second gain in the previous frame operation.

Speech coding by code-edited linear prediction

In a CELP coding scheme, p-order LPC coefficients of an input signal are transformed into n-order LPC cepctrum coefficients c.sub.j (S.sub.2), which are modified into n-order modified LPC cepstrum coefficients c.sub.j ' (S.sub.3). Log power spectral envelopes of the input signal and a masking function suited thereto are calculated (FIGS. 3B, C), then they are subjected to inverse Fourier transform to obtain n-order LPC cepstrum coefficients, respectively, (FIGS. 3D, E), then the relationship between corresponding orders of the LPC cepstrum coefficients is calculated, and the modification in step S.sub.3 is carried out on the basis of the relationship. The modified coefficients c.sub.j are inversely transformed by the method of least squares into m-order LPC coefficients for use as filter coefficients of a perceptual weighting filter. This concept is applicable to a postfilter as well.

Method for the modification of LPC coefficients of acoustic signals

An input acoustic signal is subjected to modified discrete cosine transform processing to obtain its spectrum characteristics. Linear prediction coefficients are derived from the input acoustic signal in a linear prediction coding analysis part, and the prediction coefficients are subjected to Fourier transform in a spectrum envelope calculation part to obtain the envelope of the spectrum characteristics of the input acoustic signal. In a normalization part the spectrum characteristics are normalized by the envelope thereof to obtain residual coefficients. Another normalization part normalizes the residual coefficients by a residual-coefficients envelope predicted in a residual-coefficients envelope calculation part, thereby obtaining fine structure coefficients, which are vector-quantized in a quantization part. A de-normalization part de-normalizes the quantized fine structure coefficients. The residual-coefficients envelope calculation part uses the reproduced residual coefficients to predict the envelope of residual coefficients of the subsequent frame.

Acoustic signal transform coding method and decoding method having a

An excitation vector of the previous frame stored in an adaptive codebook is cut out with a selected pitch period. The excitation vector thus cut out is repeated until one frame is formed, by which a periodic component codevector is generated. An optimum pitch period is searched for so that distortion of a reconstructed speech obtained by exciting a linear predictive synthesis filter with the periodic component codevector is minimized. Thereafter, a random codevector selected from a random codebook is cut out with the optimum pitch period and is repeated until one frame is formed, by which a repetitious random codevector is generated. The random codebook is searched for a random codevector which minimizes the distortion of the reconstructed speech which is provided by exciting the synthesis filter with the repetitious random codevector.

Speech coding and decoding methods using adaptive and random code books

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Tokorozawa, Japan

Satoshi Miki