Shmulik Markovich-Golan

Distributed Multiple Constraints Generalized Sidelobe Canceler for Fully Connected Wireless Acoustic Sensor Networks

This paper proposes a distributed multiple constraints generalized sidelobe canceler (GSC) for speech enhancement in an N-node fully connected wireless acoustic sensor network (WASN) comprising M microphones. Our algorithm is designed to operate in reverberant environments with constrained speakers (including both desired and competing speakers). Rather than broadcasting M microphone signals, a significant communication bandwidth reduction is obtained by performing local beamforming at the nodes, and utilizing only transmission channels. Each node processes its own microphone signals together with the N + P transmitted signals. The GSC-form implementation, by separating the constraints and the minimization, enables the adaptation of the BF during speech-absent time segments, and relaxes the requirement of other distributed LCMV based algorithms to re-estimate the sources RTFs after each iteration. We provide a full convergence proof of the proposed structure to the centralized GSC-beamformer (BF). An extensive experimental study of both narrowband and (wideband) speech signals verifíes the theoretical analysis.

Optimal distributed minimum-variance beamforming approaches for speech enhancement in wireless acoustic sensor networks

In multiple speaker scenarios, the linearly constrained minimum variance (LCMV) beamformer is a popular microphone array-based speech enhancement technique, as it allows minimizing the noise power while maintaining a set of desired responses towards different speakers. Here, we address the algorithmic challenges arising when applying the LCMV beamformer in wireless acoustic sensor networks (WASNs), which are a next-generation technology for audio acquisition and processing. We review three optimal distributed LCMV-based algorithms, which compute a network-wide LCMV beamformer output at each node without centralizing the microphone signals. Optimality here refers to equivalence to a centralized realization where a single processor has access to all signals. We derive and motivate the algorithms in an accessible top-down framework that reveals their underlying relations. We explain how their differences result from their different design criterion (node-specific versus common constraints sets), and their different priorities for communication bandwidth, computational power, and adaptivity. Furthermore, although originally proposed for a fully connected WASN, we also explain how to extend the reviewed algorithms to the case of a partially connected WASN, which is assumed to be pruned to a tree topology. Finally, we discuss the advantages and disadvantages of the various algorithms HighlightsApplying an LCMV beamformer in a WASN in a multiple speaker scenario is considered.Three optimal distributed LCMV-based BFs for fully connected WASNs are reviewed.We derive and motivate the algorithms in an accessible top-down common framework.Their underlying relations, advantages and disadvantages are revealed.Extending the algorithms to the case of a partially connected WASN is explained.

Geometrically Constrained TRINICON-based relative transfer function estimation in underdetermined scenarios

Speech extraction in a reverberant enclosure using a linearly-constrained minimum variance (LCMV) beamformer usually requires reliable estimates of the relative transfer functions (RTFs) of the desired source to all microphones. In this contribution, a geometrically constrained (GC)-TRINICON concept for RTF estimation is proposed. This approach is applicable in challenging multiple-speaker scenarios and in underdetermined situations, where the number of simultaneously active sources outnumbers the number of available microphone signals. As a most practically relevant and distinctive feature, this concept does not require any voice-activity-based control mechanism. It only requires coarse reference information on the target direction of arrival (DoA). The proposed GC-TRINICON method is compared to a recently proposed subspace method for RTF estimation relying on voice-activity control. Experimental results confirm the effectiveness of GC-TRINICON in realistic conditions.

Performance analysis of the covariance subtraction method for relative transfer function estimation and comparison to the covariance whitening method

Microphone array processing utilize spatial separation between the desired speaker and interference signal for speech enhancement. The transfer functions (TFs) relating the speaker component at a reference microphone with all other microphones, denoted as the relative TFs (RTFs), play an important role in beamforming design criteria such as minimum variance distortionless response (MVDR) and speech distortion weighted multichannel Wiener filter (SDW-MWF). Two common methods for estimating the RTF are surveyed here, namely, the covariance subtraction (CS) and the covariance whitening (CW) methods. We analyze the performance of the CS method theoretically and empirically validate the results of the analysis through extensive simulations. Furthermore, empirically comparing the methods performances in various scenarios evidently shows thats the CW method outperforms the CS method.

A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation

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.

Performance of the SDW-MWF With Randomly Located Microphones in a Reverberant Enclosure

Beamforming with wireless acoustic sensor networks (WASNs) has recently drawn the attention of the research community. As the number of microphones grows it is difficult, and in some applications impossible, to determine their layout beforehand. A common practice in analyzing the expected performance is to utilize statistical considerations. In the current contribution, we consider applying the speech distortion weighted multi-channel Wiener filter (SDW-MWF) to enhance a desired source propagating in a reverberant enclosure where the microphones are randomly located with a uniform distribution. Two noise fields are considered, namely, multiple coherent interference signals and a diffuse sound field. Utilizing the statistics of the acoustic transfer function (ATF), we derive a statistical model for two important criteria of the beamformer (BF): the signal to interference ratio (SIR), and the white noise gain. Moreover, we propose reliability functions, which determine the probability of the SIR and white noise gain to exceed a predefined level. We verify the proposed model with an extensive simulative study.

A weighted multichannel Wiener filter for multiple sources scenarios

The scenario of P speakers received by an M microphone array in a reverberant enclosure is considered. We extend the single source speech distortion weighted multichannel Wiener filter (SDW-MWF) to deal with multiple speakers. The mean squared error (MSE) is extended by introducing P weights, each controlling the distortion of one of the sources. The P weights enable further control in the design of the beamformer (BF). Two special cases of the proposed BF are the SDW-MWF and the linearly constrained minimum variance (LCMV)-BF. We provide a theoretical analysis for the performance of the proposed BF. Finally, we exemplify the ability of the proposed method to control the tradeoff between noise reduction (NR) and distortion levels of various speakers in an experimental study.

A sparse blocking matrix for multiple constraints GSC beamformer

Modern high performance speech processing applications incorporate large microphone arrays. Complicated scenarios comprising multiple sources, motivate the use of the linearly constrained minimum variance (LCMV) beamformer (BF) and specifically its efficient generalized sidelobe canceler (GSC) implementation. The complexity of applying the GSC is dominated by the blocking matrix (BM). A common approach for constructing the BM is to use a projection matrix to the null-subspace of the constraints. The latter BM is denoted as the eigen-space BM, and requires M2 complex multiplications, where M is the number of microphones. In the current contribution, a novel systematic scheme for constructing a multiple constraints sparse BM is presented. The sparsity of the proposed BM substantially reduces the complexity to K × (M - K) complex multiplications, where K is the number of constraints. A theoretical analysis of the signal leakage and of the blocking ability of the proposed sparse BM and of the eigen-space BM is derived. It is proven analytically, and tested for narrowband signals and for speech signals, that the blocking abilities of the sparse and of the eigen-space BMs are equivalent.

Low-Complexity Addition or Removal of Sensors/Constraints in LCMV Beamformers

We address the application of the linearly constrained minimum variance (LCMV) beamformer in sensor networks. In signal processing applications, it is common to have a redundancy in the number of nodes, fully covering the area of interest. Here we consider suboptimal LCMV beamformers utilizing only a subset of the available sensors for signal enhancement applications. Multiple desired and interfering sources scenarios in multipath environments are considered. We assume that an oracle entity determines the group of sensors participating in the spatial filtering, denoted as the active sensors. The oracle is also responsible for updating the constraints set according to either sensors or sources activity or dynamics. Any update of the active sensors or of the constraints set necessitates recalculation of the beamformer and increases the power consumption. As power consumption is a most valuable resource in sensor networks, it is important to derive efficient update schemes. In this paper, we derive procedures for adding or removing either an active sensor or a constraint from an existing LCMV beamformer. Closed-form, as well as generalized sidelobe canceller (GSC)-form implementations, are derived. These procedures use the previous beamformer to save calculations in the updating process. We analyze the computational burden of the proposed procedures and show that it is much lower than the computational burden of the straightforward calculation of their corresponding beamformers.

Combined LCMV-TRINICON Beamforming for Separating Multiple Speech Sources in Noisy and Reverberant Environments

The problem of source separation using an array of microphones in reverberant and noisy conditions is addressed. We consider applying the well-known linearly constrained minimum variance (LCMV) beamformer (BF) for extracting individual speakers. Constraints are defined using relative transfer functions (RTFs) for the sources, which are ratios of acoustic transfer functions (ATFs) between any microphone and a reference microphone. The latter are usually estimated by methods that rely on single-talk time segments where only a single source is active and on reliable knowledge of the source activity. Two novel algorithms for estimation of RTFs using the “Triple N” ICA for convolutive mixtures (TRINICON) framework are proposed, not resorting to the usually unavailable source activity pattern. The first algorithm estimates the RTFs of the sources by applying multiple two-channel geometrically constrained (GC) TRINICON units, where approximate direction of arrival information for the sources is utilized for ensuring convergence to the desired solution. The GC-TRINICON is applied to all microphone pairs using a common reference microphone. In the second algorithm, we propose to estimate RTFs iteratively using GC-TRINICON, where instead of using a fixed reference microphone as before, we suggest to use the output signals of LCMV-BFs from the previous iteration as spatially processed references with improved signal-to-interference-and-noise ratio. For both algorithms, a simple detection of noise-only time segments is required for estimating the covariance matrix of noise and interference. We conduct an experimental study in which the performance of the proposed methods is confirmed and compared to corresponding supervised methods.