EURASIP Journal on Advances in Signal Processing - Special issue on microphone array speech processing
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Speech enhancement is one of the most important issues in the communication and signal processing area. It is typically known as the suppression of additive noise rather than convoluted noise, which makes it easy to separate it from the speech. However, research for speech enhancement so far has had difficulties in enhancing speech or separating it from background noise, mostly because speech is too non-stationary to be modelled. In this literature, two speech enhancement techniques are introduced as the state of the art. One is spectral subtraction, the most popular method among all single channel speech enhancement techniques, and the other is beamforming, the spatial and temporal filtering with a microphone array. The spectral subtraction has strong attraction because it needs only one channel, its method to remove noise is quite simple, and the processed output confirms its effectiveness in improving the signal-to-noise ratio (SNR). Beamforming is an emerging technique in speech enhancement, which can simply form a beam to a speaker and enhance the speech uttered by him or her. Nevertheless, spectral subtraction has a critical weakness in that it generates unavoidable distortion, so-called musical noise, which is annoying to human ear, and beamforming cannot enhance speech sufficiently without a large number of microphones. The proposed hybrid method combines spectral subtraction and beamforming to enhance the quality and intelligibility of speech. There are some advantages of combining these methods. Musical noise, the major drawback of spectral subtraction, can be smoothed by beamforming. The speech quality, which is slightly improved by beamforming with the limited number of microphones, can be enhanced further by spectral subtraction. In addition, it is shown that the existing spectral subtraction methods can be enhanced by using psychoacoustic effects, and a voice activity detection using microphone array can be more robust. By some measures of performance evaluation, the proposed hybrid method is proved to output speech of better quality and intelligibility than each of spectral subtraction and beamforming.