{"id":152479,"date":"2000-07-01T00:00:00","date_gmt":"2000-07-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/adaptive-noise-reduction-of-speech-signals\/"},"modified":"2018-10-16T19:59:32","modified_gmt":"2018-10-17T02:59:32","slug":"adaptive-noise-reduction-of-speech-signals","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/adaptive-noise-reduction-of-speech-signals\/","title":{"rendered":"Adaptive Noise Reduction of Speech Signals"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We propose a new adaptive speech noise removal algorithm based on a two-stage Wiener filtering. A first Wiener filter is used to produce a smoothed estimate of the a priori signal-to-noise ratio (SNR), aided by a classifier that separates speech from noise frames, and a second Wiener filter is used to generate the final output. Spectral analysis and synthesis is performed by a modulated complex lapped transform (MCLT). For noisy speech at a low 10 dB input SNR, for example, the proposed algorithm can achieve on average about 13 dB noise-to-mask ratio (NMR) reduction, or about 6 dB SNR improvement.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We propose a new adaptive speech noise removal algorithm based on a two-stage Wiener filtering. A first Wiener filter is used to produce a smoothed estimate of the a priori signal-to-noise ratio (SNR), aided by a classifier that separates speech from noise frames, and a second Wiener filter is used to generate the final output. 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