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.

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A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation

Today, we are often surrounded by devices with one or more microphones, such as smartphones, laptops, and wireless microphones. If they are part of an acoustic sensor network, their distribution in the environment can be beneficially exploited for various speech processing tasks. However, applications like speaker localization, speaker tracking, and speech enhancement by beamforming avail themselves of the geometrical configuration of the sensors. Therefore, acoustic microphone geometry calibration has recently become a very active field of research. This article provides an application-oriented, comprehensive survey of existing methods for microphone position self-calibration, which will be categorized by the measurements they use and the scenarios they can calibrate. Selected methods will be evaluated comparatively with real-world recordings.

Acoustic Microphone Geometry Calibration: An overview and experimental evaluation of state-of-the-art algorithms

The challenge of blindly resynchronizing the data acquisition processes in a wireless acoustic sensor network (WASN) is addressed in this paper. The sampling rate offset (SRO) is precisely modeled as a time scaling. The applicability of a wideband correlation processor for estimating the SRO, even in a reverberant and multiple source environment, is presented. An explicit expression for the ambiguity function, which in our case involves time scaling of the received signals, is derived by applying truncated band-limited interpolation. We then propose the recursive band-limited interpolation (RBI) algorithm for recursive SRO estimation. A complete resynchronization scheme utilizing the RBI algorithm, in parallel with the SRO compensation module, is presented. The resulting resynchronization method operates in the time domain in a sequential manner and is, thus, capable of tracking a potentially time-varying SRO. We compared the performance of the proposed RBI algorithm to other available methods in a simulation study. The importance of resynchronization in a beamforming application is demonstrated by both a simulation study and experiments with a real WASN. Finally, we present an experimental study evaluating the expected SRO level between typical data acquisition devices.

Blind Synchronization in Wireless Acoustic Sensor Networks

This paper proposes novel randomized gossip-consensus-based sync (RGCS) algorithms to realize efficient time calibration in dynamic wireless sensor networks (WSNs). First, the unreliable links are described by stochastic connections, reflecting the characteristic of changing connectivity gleaned from dynamic WSNs. Secondly, based on the mutual drift estimation, each pair of activated nodes fully adjusts clock rate and offset to achieve network-wide time synchronization by drawing upon the gossip consensus approach. The converge-to-max criterion is introduced to achieve a much faster convergence speed. The theoretical results on the probabilistic synchronization performance of the RGCS are presented. Thirdly, a Revised-RGCS is developed to counteract the negative impact of bounded delays, because the uncertain delays are always present in practice and would lead to a large deterioration of algorithm performances. Finally, extensive simulations are performed on the MATLAB and OMNeT++ platform for performance evaluation. Simulation results demonstrate that the proposed algorithms are not only efficient for synchronization issues required for dynamic topology changes but also give a better performance in terms of converging speed, collision rate, and the robustness of resisting delay, and outperform other existing protocols.

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Randomized and efficient time synchronization in dynamic wireless sensor networks: a gossip-consensus-based approach

Microphone arrays allow to exploit the spatial coherence between simultaneously recorded microphone signals, e.g., to perform speech enhancement, i.e., to extract a speech signal and reduce background noise. However, in systems where the microphones are not sampled in a synchronous fashion, as it is often the case in wireless acoustic sensor networks, a sampling rate offset (SRO) exists between signals recorded in different nodes, which severely affects the speech enhancement performance. To avoid this performance reduction, the SRO should be estimated and compensated for. In this paper, we propose a new approach to blind SRO estimation for an asynchronous wireless acoustic sensor network, which exploits the phase drift of the coherence between the asynchronous microphones signals. We utilize the fact that the SRO causes a linearly increasing time delay between two signals and hence a linearly increasing phase-shift in the short-time Fourier transform domain. The increasing phase shift, observed as a phase drift of the coherence between the signals, is used in a weighted least-squares framework to estimate the SRO. This method is referred to as least-squares coherence drift (LCD). Experimental results in different real-world recording and simulated scenarios show the effectiveness of LCD compared to different benchmark methods. The LCD is effective even for short signal segments. We finally demonstrate that the use of the LCD within a conventional compensation approach eliminates the performance loss due to SRO in a speech enhancement algorithm based on the multichannel Wiener filter.

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Blind Sampling Rate Offset Estimation for Wireless Acoustic Sensor Networks Through Weighted Least-Squares Coherence Drift Estimation

A combined hardware-software approach for acoustic sensor network synchronization

"In this paper we present an approach for synchronizing the sampling clocks of distributed microphones over a wireless network. The proposed system uses a two stage procedure. It first employs a two-way message exchange algorithm to estimate the clock phase and frequency difference between two nodes and then uses a gossiping algorithmto estimate a virtual master clock, to which all sensor nodes synchronize. Simulation results are presented for networks of different topology and size, showing the effectiveness of our approach."

Patrick Jebramcik

Papers

A combined hardware-software approach for acoustic sensor network synchronization

A gossiping approach to sampling clock synchronization in wireless acoustic sensor networks