Abstract
This paper introduces two short-time spectral amplitude estimators for speech enhancement with multiple microphones. Based on joint Gaussian models of speech and noise Fourier coefficients, the clean speech amplitudes are estimated with respect to the MMSE or the MAP criterion. The estimators outperform single microphone minimum mean square amplitude estimators when the speech components are highly correlated and the noise components are sufficiently uncorrelated. Whereas the first MMSE estimator also requires knowledge of the direction of arrival, the second MAP estimator performs a direction-independent noise reduction. The estimators are generalizations of the well-known single channel MMSE estimator derived by Ephraim and Malah (1984) and the MAP estimator derived by Wolfe and Godsill (2001), respectively.
Highlights
Speech communication appliances such as voice-controlled devices, hearing aids, and hands-free telephones often suffer from poor speech quality due to background noise and room reverberation
We propose the extensions of two single channel speech spectral amplitude estimators for the use in microphone array noise reduction
Compared to the 1d-minimum mean square error (MMSE), the Md-MMSE and Md-maximum a posteriori (MAP) deliver a lower noise reduction amount at a higher speech quality when applied to speech disturbed by coherent noise
Summary
Speech communication appliances such as voice-controlled devices, hearing aids, and hands-free telephones often suffer from poor speech quality due to background noise and room reverberation Multiple microphone techniques such as beamformers can improve the speech quality and intelligibility by exploiting the spatial diversity of speech and noise sources. Single microphone speech enhancement frequency domain algorithms are comparably robust against reverberation and multiple sources They can achieve high noise reduction only at the expense of moderate speech distortion. We propose the extensions of two single channel speech spectral amplitude estimators for the use in microphone array noise reduction. A minimum mean square estimator that evaluates the expectation of the speech spectral amplitude conditioned on all noisy complex DFT coefficients is described.
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