Speech enhancement and separation are core problems in audio signal processing, with commercial applications in devices as diverse as mobile phones, conference call systems, hands-free systems, or hearing aids. In addition, they are crucial preprocessing steps for noise-robust automatic speech and speaker recognition. Many devices now have two to eight microphones. The enhancement and separation capabilities offered by these multichannel interfaces are usually greater than those of single-channel interfaces. Research in speech enhancement and separation has followed two convergent paths, starting with microphone array processing and blind source separation, respectively. These communities are now strongly interrelated and routinely borrow ideas from each other. Yet, a comprehensive overview of the common foundations and the differences between these approaches is lacking at present. In this paper, we propose to fill this gap by analyzing a large number of established and recent techniques according to four transverse axes: 1 the acoustic impulse response model, 2 the spatial filter design criterion, 3 the parameter estimation algorithm, and 4 optional postfiltering. We conclude this overview paper by providing a list of software and data resources and by discussing perspectives and future trends in the field.

/pdf/a-consolidated-perspective-on-multimicrophone-speech-qnbaqxmtvq.pdf

A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation

The quality of speech signals recorded in an enclosure can be severely degraded by room reverberation. In this paper, we focus on a class of blind batch methods for speech dereverberation in a noiseless scenario with a single source, which are based on multi-channel linear prediction in the short-time Fourier transform domain. Dereverberation is performed by maximum-likelihood estimation of the model parameters that are subsequently used to recover the desired speech signal. Contrary to the conventional method, we propose to model the desired speech signal using a general sparse prior that can be represented in a convex form as a maximization over scaled complex Gaussian distributions. The proposed model can be interpreted as a generalization of the commonly used time-varying Gaussian model. Furthermore, we reformulate both the conventional and the proposed method as an optimization problem with an ep-norm cost function, emphasizing the role of sparsity in the considered speech dereverberation methods. Experimental evaluation in different acoustic scenarios show that the proposed approach results in an improved performance compared to the conventional approach in terms of instrumental measures for speech quality.

Multi-channel linear prediction-based speech dereverberation with sparse priors

In this contribution, we focus on the problem of power spectral density (PSD) estimation from multiple microphone signals in reverberant and noisy environments. The PSD estimation method proposed in this paper is based on the maximum likelihood (ML) methodology. In particular, we derive a novel ML PSD estimation scheme that is suitable for sound scenes which besides speech and reverberation consists of an additional noise component whose second-order statistics are known. The proposed algorithm is shown to outperform an existing similar algorithm in terms of PSD estimation accuracy. Moreover, it is shown numerically that the mean-squared estimation error achieved by the proposed method is near the limit set by the corresponding Cramer--Rao lower bound. The speech dereverberation performance of a multichannel Wiener filter based on the proposed PSD estimators is measured using several instrumental measures and is shown to be higher than when the competing estimator is used. Moreover, we perform a speech intelligibility test where we demonstrate that both the proposed and the competing PSD estimators lead to similar intelligibility improvements.

/pdf/maximum-likelihood-psd-estimation-for-speech-enhancement-in-3p5bc42iey.pdf

Maximum likelihood PSD estimation for speech enhancement in reverberation and noise

Room reverberation and background noise severely degrade the quality of hands-free speech communication systems. In this work, we address the problem of combined speech dereverberation and noise reduction using a variational Bayesian (VB) inference approach. Our method relies on a multichannel state-space model for the acoustic channels that combines frame-based observation equations in the frequency domain with a first-order Markov model to describe the time-varying nature of the room impulse responses. By modeling the channels and the source signal as latent random variables, we formulate a lower bound on the log-likelihood function of the model parameters given the observed microphone signals and iteratively maximize it using an online expectation-maximization approach. Our derivation yields update equations to jointly estimate the channel and source posterior distributions and the remaining model parameters. An inspection of the resulting VB algorithmfor blind equalization and channel identification (VB-BENCH) reveals that the presented framework includes previously proposed methods as special cases. Finally, we evaluate the performance of our approach in terms of speech quality, adaptation times, and speech recognition results to demonstrate its effectiveness for a wide range of reverberation and noise conditions.

Variational Bayesian inference for multichannel dereverberation and noise reduction

Presents corrections to the paper, "A Novel Approach Based on Marine Radar Data Analysis for High-Resolution Bathymetry Map Generation."

Correction to "Maximum Likelihood PSD Estimation for Speech Enhancement in Reverberation and Noise" [Sep 16 1599-1612]

The blind identification of single-input multiple-output (SIMO) systems is often performed by exploiting a cross-relation (CR) between channel pairs. It has been shown that this property allows an estimation of channel impulse responses up to a common gain factor if certain identifiability conditions are met. In this case, the estimated channels can be evaluated by a gain-compensated system distance known as normalized projection misalignment (NPM). Current algorithms for blind channel identification, however, suffer in the presence of insufficient channel diversity and observation noise. In this paper, we first demonstrate that in the absence of noise the CR identification error for channels with exact common zeros is given by a single-channel pole-zero transfer function. Next, we extend our analysis to the realistic case of near-common zeros and noise for which we show that the effective error can still be approximated by a common transfer function as long as the distance between the channel zeros remains below a signal-to-noise ratio-dependent threshold. A finite impulse response (FIR) modeling of the error then enables us to define a common-filter-error-compensated system distance, termed normalized filter-projection misalignment (NFPM), which establishes a natural extension to the NPM analysis. By finally considering realistic channels, which we blindly estimate with the adaptive multichannel least mean-square (MCLMS) algorithm, we demonstrate that the NFPM reliably reaches the noise floor, confirming that the effective error can be approximated by a common FIR filter.

Cross-Relation-Based Blind SIMO Identifiability in the Presence of Near-Common Zeros and Noise

This paper presents an online dereverberation algorithm that is derived within the maximum-likelihood expectation-maximization (ML-EM) framework. We formulate an overlap-save observation model for the multichannel blind problem in the DFT-domain. The modeling of acoustic channel impulse responses as random variables with a first-order Markov property facilitates the ensuing algorithm to cope with time-varying conditions. We then show that the ML-EM learning rules for the multichannel state-space model at hand take the form of a recursive posterior estimator for the channels, followed by an equalization stage for recovering the speech signal subject to an expectation with respect to the estimated channel posterior. Our derivation thus results in an iterative ML algorithm for blind equalization and channel identification (ML-BENCH) which comprises two distinct and coupled subsystems. The dereverberation performance of the proposed system is evaluated by considering spectrograms and instrumental quality measures.

An expectation-maximization algorithm for multichannel adaptive speech dereverberation in the frequency-domain

This paper presents an iterative approach to multi-channel blind system identification. The concept includes two subsystems for channel identification and equalization which are specifically tailored for robust mutual interaction. This robustness is a natural prerequisite for the convergence of iterative systems when no suitable a priori information about the channels or the input signal is available. For the channel identification subsystem, we introduce a supervised, variable stepsize LMS-type adaptive algorithm which is able to identify the channels from an estimated input signal. We then show that matched filter arrays can be utilized as the equalization subsystem to estimate the input signal on the basis of the identified channels. Eventually, we demonstrate that the iterative coupling of both subsystems converges to a solution, the quality of which can be predicted from our comprehensive analysis of the independent subsystems.

Robust subsystems for iterative multichannel blind system identification and equalization

A Maximum A Posteriori Approach to Multichannel Speech Dereverberation and Denoising

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