New methods for adaptive noise suppression
نویسندگان
چکیده
We propose three new adaptive noise suppression algorithms for enhancing noise-corrupted speech: smoothed spectral subtraction (SSS), vector quantization of line spectral frequencies (VQ-LSF), and modiied Wiener ltering (MWF). SSS is an improved version of the well-known spectral subtraction algorithm, while the other two methods are based on generalized Wiener ltering. We have compared these three algorithms with each other and with spectral subtraction on both simulated noise and actual car noise. All three proposed methods perform substantially better than spectral subtraction, primarily because of the absence of any musical noise artifacts in the processed speech. Listening tests showed preference for MWF and SSS over VQ-LSF. Also, MWF provides a much higher mean opinion score (MOS) than does spectral subtraction. Finally, VQ-LSF provides a relatively good spectral match to the clean speech, and may, therefore, be better suited for speech recognition.
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