Data processing: speech signal processing – linguistics – language – Speech signal processing – For storage or transmission
Reexamination Certificate
2003-05-28
2004-08-24
McFadden, Susan (Department: 2655)
Data processing: speech signal processing, linguistics, language
Speech signal processing
For storage or transmission
C704S220000, C704S222000
Reexamination Certificate
active
06782359
ABSTRACT:
BACKGROUND
This invention relates to digital voice coders performing at relatively low voice rates but maintaining high voice quality. In particular, it relates to improved multipulse linear predictive voice coders.
The multipulse coder incorporates the linear predictive all-pole filter (LPC filter). The basic function of a multipulse coder is finding a suitable excitation pattern for the LPC all-pole filter which produces an output that closely matches the original speech waveform. The excitation signal is a series of weighted impulses. The weight values and impulse locations are found in a systematic manner. The selection of a weight and location of an excitation impulse is obtained by minimizing an error criterion between the all-pole filter output and the original speech signal. Some multipulse coders incorporate a perceptual weighting filter in the error criterion function. This filter serves to frequency weight the error which in essence allows more error in the format regions of the speech signal and less in low energy portions of the spectrum. Incorporation of pitch filters improve the performance, of multipulse speech coders. This is done by modeling the long term redundancy of the speech signal thereby allowing the excitation signal to account for the pitch related properties of the signal.
SUMMARY
Linear predictive coding (LPC) filter parameters are determined for use in encoding a voice signal. Samples of a speech signal using a z-transform function are pre-emphasized. The pre-emphasized samples are analyzed to produce LPC reflection coefficients. The LPC reflection coefficients are quantized by a voiced quantizer and by an unvoiced quantizer producing sets of quantized reflection coefficients. Each set is converted into respective spectral coefficients. The set which produces a smaller lag-spectral distance is determined. The determined set is selected to encode the voice signal.
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Lin Daniel
McCarthy Brian M.
InterDigital Technology Corporation
McFadden Susan
Volpe and Koenig P.C.
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