Data processing: speech signal processing – linguistics – language – Speech signal processing – For storage or transmission
Reexamination Certificate
2000-11-22
2002-10-08
Dorvil, Richemond (Department: 2641)
Data processing: speech signal processing, linguistics, language
Speech signal processing
For storage or transmission
C704S233000
Reexamination Certificate
active
06463408
ABSTRACT:
BACKGROUND OF THE INVENTION
The present invention relates generally to radio communications and, more particularly, to systems and methods that reduce background noise associated with speech signals.
Over the past decade, the use of mobile terminals has increased dramatically. So too have the features associated with these devices. Presently, mobile terminals may be used to place and receive telephone calls, connect to the Internet, send and receive pages and facsimiles, etc. from almost any location in the world. As the demand for these devices increases, designers of mobile terminals are continually seeking new ways to improve performance.
BRIEF SUMMARY OF THE INVENTION
Systems and methods, consistent with the present invention, estimate power spectral densities of speech signals used for reducing noise. The systems and methods allow the speech signals' power spectral density to be approximated in even low signal-to-noise situations, resulting in improved noise reduction.
In accordance with the invention as embodied and broadly described herein, a method for determining a power spectral density associated with an audio signal that includes a speech signal and/or a noise signal comprises updating an autocorrelation function of the audio signal from samples in the audio signal; estimating an autocorrelation function of the speech signal from the updated autocorrelation function of the audio signal; calculating a power spectral density of the speech signal using the estimated autocorrelation function; and determining the power spectral density of the audio signal from the calculated power spectral density of the speech signal.
In another implementation consistent with the present invention, a noise reduction system comprises a converter, a power spectral estimator, and a filter. The converter receives an audio signal and divides the audio signal into multiple frames. Each of the frames comprises a mixed signal containing a speech signal and/or a noise signal. The power spectral estimator determines a power spectral density associated with the mixed signal for each of the frames by updating an autocorrelation function of the mixed signal from samples in the frame, estimating an autocorrelation function of the speech signal in the frame from the updated autocorrelation function, determining a power spectral density of the speech signal using the estimated autocorrelation function, and determining a power spectral density of the mixed signal using the determined power spectral density of the speech signal. The filter performs spectral subtraction on the frames using the determined power spectral densities associated with the mixed signals of the frames to reduce noise associated with the audio signal.
In a further implementation consistent with the present invention, a computer-readable medium stores instructions executable by one or more processors to perform a method for reducing noise associated with an audio signal. The audio signal comprises a speech signal and/or a noise signal. The computer-readable medium comprises instructions for updating an autocorrelation function of the audio signal from samples in the audio signal; instructions for determining an autocorrelation function of the speech signal from the updated autocorrelation function of the audio signal; instructions for determining a power spectral density of the speech signal using the estimated autocorrelation function; instructions for determining the power spectral density of the audio signal from the calculated power spectral density of the speech signal; and instructions for using the power spectral density of the audio signal to reduce noise associated with the audio signal.
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Krasny Leonid
Oraintara Soontorn
Dorvil Richemond
Ericsson Inc.
Harrity & Synder, L.L.P.
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