Analysis and Comparison of Multichannel Noise Reduction Methods in a Common Framework

Abstract

Abstract-Noise reduction for speech enhancement is a useful technique, but in general it is a challenging problem. While a single-channel algorithm is easy to use in practice, it inevitably introduces speech distortion to the desired speech signal while reducing noise. Today, the explosive growth in computational power and the continuous drop in the cost and size of acoustic electric transducers are driving the interest of employing multiple microphones in speech processing systems. This opens new opportunities for noise reduction. In this paper, we present an analysis of three multichannel noise reduction algorithms, namely Wiener filter, subspace, and spatial-temporal prediction, in a common framework. We intend to investigate whether it is possible for the multichannel noise reduction algorithms to reduce noise without speech distortion. Finally, we justify what we learn via theoretical analyses by simulations using real impulse responses measured in the varechoic chamber at Bell Labs.Index Terms-Microphone array signal processing, multichannel subspace method, multichannel Wiener filter, noise reduction, spatial prediction, speech enhancement.

Frame-Based Space-Time Covariance Matrix Estimation for Polynomial Eigenvalue Decomposition-Based Speech Enhancement

“…, s(n − J)] T is the anechoic speech signal, v m (n) is additive noise, and [•] T is the transpose operator. The noise signals are assumed to be zero-mean, nonperfectly coherent with each other, and uncorrelated with the source signal [20]. Stacking the microphone signals gives…”