Real-time Mozer phase recoding using a neural-network for speech compression
AssigneeHarris Corporation
InventorMichael Thomas Kurdziel
Life of this patent
Estimating the term…
Abstract
A system and method for compressing speech using an artificial neural network to calculate the recoded phase vector (Mozer code) resulting from the spectral magnitude-to-phase transformation. Raw speech is equalized to remove the spectral tilt and segmented into analysis frames. The spectral magnitudes of each frame segment are determined at a plurality of points by a Fourier Transform, normalized, and applied to a neural net magnitude-to-phase transform calculator to provide a recoded phase vector. An Inverse Discrete Fourier Transform is used to calculate the new recoded speech waveform in which the two quarters with minimum power are zeroed to produce the compressed speech output signal.
Claims16
A method of compressing speech comprising the steps of:
- (a) equalizing the spectral magnitudes of a raw speech waveform;
- (b) segmenting the equalized raw speech into initial analysis frames;
- (c) detecting the pitch of the raw speech in each segment;
- (d) associating the detected pitch with each frame segment;
- (e) determining the spectral magnitudes of each frame segment by a Discrete Fourier Transform or FFT at a plurality of points;
- (f) normalizing the output signal from the FFT;
- (g) applying the normalized FFT signal to a neural net magnitude to phase transform calculator to provide a recoded phase vector.
- (h) calculating a new recoded speech waveform by use of an Inverse Discrete Fourier Transform and the un-normalized spectral magnitudes determined in the FFT;
- (i) zeroing two quarters with minimum power to produce a compressed speech output signal;
- and
- (j) selecting one of the two remaining quarters to characterize the entire frame.
A method of compressing speech comprising the steps of:
- (a) equalizing the spectral magnitudes of a raw speech waveform;
- (b) segmenting the equalized raw speech into initial analysis frames;
- (c) detecting the pitch of the raw speech in each segment;
- (d) associating the detected pitch with each frame segment;
- (e) determining the spectral magnitudes of each frame segment by a Discrete Fourier Transform or FFT at a plurality of points;
- (f) normalizing the output signal from the FFT;
- (g) applying the normalized FFT signal to a neural net magnitude to phase transform calculator to provide a recoded phase vector.
- (h) calculating a new recoded speech waveform by use of an Inverse Discrete Fourier Transform and the normalized spectral magnitudes with a gain constant associated with each segment;
- (i) zeroing two quarters with minimum power to produce a compressed speech output signal;
- and
- (j) selecting one of the two remaining quarters to characterize the entire frame.
A method of increasing the speed of compressing speech comprising the steps of:
- (a) equalizing the spectral magnitudes of a raw speech waveform;
- (b) segmenting the equalized raw speech into initial analysis frames;
- (c) determining the spectral magnitudes of each frame segment by a Discrete Fourier Transform or FFT at a plurality points assuming a constant segment length;
- (d) normalizing the output signal from the FFT;
- (e) applying the normalized FFT signal to a neural net magnitude to phase transform calculator to provide a recoded phase vector.
- (f) calculating a new recoded speech waveform by use of an Inverse Discrete Fourier Transform and the un-normalized spectral magnitudes determined in the FFT;
- (g) zeroing two quarters with minimum power to produce a compressed speech output signal;
- and
- (h) selecting one of the two remaining quarters to characterize the entire frame.
A method of compressing speech comprising the steps of:
- (a) filtering raw speech to equalize the spectral amplitudes to remove any spectral tilt;
- (b) determining the pitch of the filtered speech (assume a constant if the speech is unvoiced)
- (c) segmenting the filtered speech into frames having a length proportional to the detected pitch period;
- (d) determining the spectral magnitudes of each segment by a FFT;
- (e) calculating the magnitude to phase transform with a neural network to produce the recoded phase vector;
- (f) processing the calculated magnitude to phase vector with the spectral magnitudes of the raw speech with an Inverse Discrete Fourier Transform to provide a recoded symmetric waveform;
- and
- (g) zeroing the first and fourth quarter waveforms.
In a method of compressing speech in the time domain waveform for time periods less than about 20 ms by the manipulation of phase parameters, the improvement comprising the step of using an artificial neural network trained to closely approximate the magnitude to phase vector transform in the conversion of spectral magnitudes within an analysis frame to a phase vector.
Dependencies are inferred from the claim text.