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Content Provider | IEEE Xplore Digital Library |
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Author | Denda, Y. Nishiura, T. Kawahara, H. |
Copyright Year | 2003 |
Description | Author affiliation: Fac. of Syst. Eng., Wakayama Univ., Japan (Denda, Y.; Nishiura, T.; Kawahara, H.) |
Abstract | It is very important to capture distant-talking speech with high quality for teleconferencing systems or voice-controlled systems. For this purpose, microphone array steering and Fourier spectral subtraction, for example, are ideal candidates. A combination technique using both microphone array steering and Fourier spectral subtraction has also been proposed to improve performance. However, it is difficult for the conventional approach to reduce non-stationary noise, although it is easy to robustly reduce stationary noise. To cope with this problem, we propose a new combination technique with microphone array steering and Fourier/wavelet spectral subtraction. Wavelet spectral subtraction promises to effectively reduce non-stationary noise, because the wavelet transform admits a variable time-frequency resolution on each frequency band. As a result of evaluation experiments in a real room, we confirmed that the proposed combination technique provides better performance of the ASR (automatic speech recognition) and NRR (noise reduction rate) than the conventional combination technique in a directional and a diffused noise environment. |
Sponsorship | IEEE Signal Processing Soc |
Starting Page | 593 |
Ending Page | 596 |
File Size | 276228 |
Page Count | 4 |
File Format | |
ISBN | 0780379977 |
DOI | 10.1109/SSP.2003.1289545 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2003-09-28 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Microphone arrays Speech recognition Acoustic noise Noise reduction Working environment noise Frequency Automatic speech recognition Additive noise Teleconferencing Wavelet transforms |
Content Type | Text |
Resource Type | Article |
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