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Abstract

Properties of human audio perception are used to perform spectral and time masking to reduce perceived loudness of noise added to the speech signal. A signal is divided temporally into blocks which are then passed through notch filters to remove narrow frequency band components of the noise. Each block is then appended to part of the previous block in a manner which avoids block boundary discontinuities. An FFT is then performed on the resulting larger block, after which the spectral components of the signal are fed to a background noise estimator. Each frequency component of the signal is analyzed with respect to the background noise to determine, within various confidence levels, whether it is pure noise or a noise-and-signal combination. The frequency band's gain function is determined, based on the confidence levels. A spectral valley finder detects and fills in spectral valleys in the frequency component gain function, after which the function is used to modify the magnitude components of the FFT. An inverse FFT then maps the signal back from the frequency domain to the time domain to give a frame of noise-reduced signal. This signal is then multiplied by a temporal window and joined to the previous frame's signal to derive the output.

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First Claim