Spectral subtraction using reduced delay convolution and adaptive averaging
In hands-free speech communication, the signal-to-noise ratio (SNR) is often poor, which makes it difficult to have a relaxed conversation. By using noise suppression, the conversation quality can be improved. This paper describes a noise suppression algorithm based on spectral subtraction. The meth...
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Veröffentlicht in: | IEEE transactions on speech and audio processing 2001-11, Vol.9 (8), p.799-807 |
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Sprache: | eng |
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Zusammenfassung: | In hands-free speech communication, the signal-to-noise ratio (SNR) is often poor, which makes it difficult to have a relaxed conversation. By using noise suppression, the conversation quality can be improved. This paper describes a noise suppression algorithm based on spectral subtraction. The method employs a noise and speech-dependent gain function for each frequency component. Proper measures have been taken to obtain a corresponding causal filter and also to ensure that the circular convolution originating from fast Fourier transform (FFT) filtering yields a truly linear filtering. A novel method that uses spectrum-dependent adaptive averaging to decrease the variance of the gain function is also presented. The results show a 10-dB background noise reduction for all input SNR situations tested in the range -6 to 16 dB, as well as improvement in speech quality and reduction of noise artifacts as compared with conventional spectral subtraction methods. |
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ISSN: | 1063-6676 2329-9290 1558-2353 1558-2353 2329-9304 |
DOI: | 10.1109/89.966083 |