Abstract
In this chapter we address the limitations of current approaches to using microphone arrays for speech acquisition and advocate the development of multichannel techniques which employ non-traditional processing and an explicit model of the speech signal. The goal is to combine the advantages of spatial filtering achieved through beamforming with knowledge of the desired time-series attributes and intuitive nonlinear processing. We then offer a multi-channel algorithm which incorporates these principles. The enhanced speech is synthesized using a linear predictive filter. The excitation signal is computed from a nonlinear wavelet-domain process. It uses extrema clustering of the multi-channel speech data to discriminate portions of the linear prediction residual produced by the desired speech signal from those due to multi path effects and uncorrelated noise. The algorithm is shown to be capable of identifying and attenuating reverberant portions of the speech signal and reducing the effects of additive noise.
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Brandstein, M.S., Griebel, S.M. (2000). Nonlinear, Model-Based Microphone Array Speech Enhancement. In: Gay, S.L., Benesty, J. (eds) Acoustic Signal Processing for Telecommunication. The Springer International Series in Engineering and Computer Science, vol 551. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-8644-3_12
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