A Multi-Channel Noise Estimator Based on Improved Minima Controlled Recursive Averaging for Speech Enhancement

This article introduces an extension of the improved minima-controlled recursive averaging noise estimation from single to multi-channel speech enhancement systems. With the spatial information of microphone array signals being fully exploited, more accurate estimate of the noise spectrum can be obtained over the single-channel counterpart. Computer simulation demonstrates superior performance of the proposed noise estimator in terms of noise tracking performance and noise estimation error. Furthermore, the use of the proposed technique with the multi-channel Wiener filter yields improved signal-to-noise ratio and speech distortion

Assistive listening headsets for high noise environments: Protection and communication

© 2015 IEEE. In industrial noise environments, the use of assistive listening headsets is a means to provide adequate access to voice communication while wearing hearing protection. This paper presents a performance evaluation and comparison of two different methods to provide the binaural speech enhancement in real industrial noise scenarios. The investigated binaural methods based on differential beamforming and multichannel Wiener filter show different strengths and weaknesses. A transient noise suppression algorithm is also proposed and evaluated. Performance evaluation shows that this algorithm, together with the binaural multi-channel Wiener filter approach, can successfully reduce the hammering noise. This can be observed from the PESQ scores and the signal characteristics

Instantaneous PSD Estimation for Speech Enhancement based on Generalized Principal Components

Power spectral density (PSD) estimates of various microphone signal components are essential to many speech enhancement procedures. As speech is highly non-nonstationary, performance improvements may be gained by maintaining time-variations in PSD estimates. In this paper, we propose an instantaneous PSD estimation approach based on generalized principal components. Similarly to other eigenspace-based PSD estimation approaches, we rely on recursive averaging in order to obtain a microphone signal correlation matrix estimate to be decomposed. However, instead of estimating the PSDs directly from the temporally smooth generalized eigenvalues of this matrix, yielding temporally smooth PSD estimates, we propose to estimate the PSDs from newly defined instantaneous generalized eigenvalues, yielding instantaneous PSD estimates. The instantaneous generalized eigenvalues are defined from the generalized principal components, i.e. a generalized eigenvector-based transform of the microphone signals. We further show that the smooth generalized eigenvalues can be understood as a recursive average of the instantaneous generalized eigenvalues. Simulation results comparing the multi-channel Wiener filter (MWF) with smooth and instantaneous PSD estimates indicate better speech enhancement performance for the latter. A MATLAB implementation is available online