Clustered Blind Beamforming From Ad-Hoc Microphone Arrays

Himawan, Ivan; McCowan, Iain; Sridharan, Sridha

doi:10.1109/tasl.2010.2055560

Cited by 59 publications

(41 citation statements)

References 25 publications

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“…In contrast to conventional microphone arrays with co-located elements, DMAs are emerging as promising systems with the potential to solve challenging speech processing tasks efficiently, such as enhancement [3][4][5] and localization [2]. This fact is essentially justified by their scalability and spatial coverage.…”

Section: Introductionmentioning

confidence: 99%

“…This approach was proposed for fully-connected and tree topology configurations and is claimed to be applicable to wireless sensor networks in general, and DMAs, in particular. In [5], distributed beamforming is proposed based on distributed microphone clustering and selection; only microphones close to each other and to the target speech source were selected to enhance it. In the aforementioned applications, the accurate tracking of the presence (or absence) of speech energy plays a fundamental role.…”

Section: Introductionmentioning

confidence: 99%

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Distributed microphone array processing for speech source separation with classifier fusion

Souden

Kinoshita

Delcroix

et al. 2012

2012 IEEE International Workshop on Machine Learning for Signal Processing

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We propose a new approach for clustering and separating competing speech signals using a distributed microphone array (DMA). This approach can be viewed as an extension of expectationmaximization (EM)-based source separation to DMAs. To achieve distributed processing, we assume the conditional independence (with respect to sources' activities) of the normalized recordings of different nodes. By doing so, only the posterior probabilities of sources' activities need to be shared between nodes. Consequently, the EM algorithm is formulated such that at the expectation step, local posterior probabilities are estimated locally and shared between nodes. In the maximization step, every node fuses the received probabilities via either product or sum rules and estimates its local parameters. We show that, even if we make binary decisions (presence/absence of speech) during EM iterations instead of transmitting continuous posterior probability values, we can achieve separation without causing significant speech distortion. Our preliminary investigations demonstrate that the proposed processing technique approaches the centralized solution and can outperform Oracle best node-wise clustering in terms of objective source separation metrics.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed microphone array processing for speech source separation with classifier fusion

Souden

Kinoshita

Delcroix

et al. 2012

2012 IEEE International Workshop on Machine Learning for Signal Processing

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“…The use of additional microphone nodes (e.g. from tablets, smartphones, or dedicated devices) allows a significant improvement in the enhancement of the recorded speech signals [5], [11]- [14]. Furthermore, WASNs are an enabling technology for speech communication in noisy and dynamic environments such as airports, factories, stock markets, etc.…”

Section: Example Applicationsmentioning

confidence: 99%

“…Basically, this means that adding an extra microphone has no (or limited) impact on the computational load or data traffic at the nodes that are not directly connected to this extra node. Distributed algorithms that allow simply connected networks are usually scalable [11], [14], [20], [21]. 5) Microphone subset selection: In large-scale WASNs, sufficient performance can often be obtained by only using a subset of microphones (e.g., microphones that are close to a desired sound source).…”

Section: Core Challengesmentioning

confidence: 99%

Applications and trends in wireless acoustic sensor networks: A signal processing perspective

Bertrand

2011

2011 18th IEEE Symposium on Communications and Vehicular Technology in the Benelux (SCVT)

214

153

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Wireless microphone networks or so-called wireless acoustic sensor networks (WASNs) are a next-generation technology for audio acquisition and processing. As opposed to traditional microphone arrays that sample a sound field only locally, often at large distances from the relevant sound sources, WASNs allow to use many more microphones to cover a large area of interest. However, the design of such WASNs is very challenging, especially for real-time audio acquisition and signal enhancement due to the significant data traffic in the network. There is a need for scalable solutions, both on the signal processing level and on the network-communication level. In this paper, we give an overview of applications and trends in the field of WASNs, and we address the core challenges that need to be tackled. We mainly focus on the signal processing level, and we explain how advances in the area of signal processing can relax the high-demanding constraints on the network layer design. Furthermore, we address the interaction between the application layer and the network layer, and we explain why cross-layer design can be important to improve the performance of WASN applications.

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“…Rather than performing an iterative estimation of each individual sample of the desired signal, DSE algorithms iteratively improve the in-network fusion rules in a timerecursive fashion. DSE algorithms typically operate at higher data rates (compared to DPE algorithms) and often require specific network topologies such as, e.g., fully connected, star, or tree topologies to avoid feedback in the signal fusion paths [1]- [5], [14], [15], [18], [25], [28]. In such topology-controlled networks, the true benefit of a distributed implementation then lies in the in-network fusion/compression of the collected sensor data, i.e., nodes exchange only single-channel (scalar) signal observations instead of multi-channel (vector) signal observations.…”

Section: A Distributed Signal Estimation In Wireless Sensor Networkmentioning

confidence: 99%

Distributed LCMV Beamforming in a Wireless Sensor Network With Single-Channel Per-Node Signal Transmission

Bertrand

Moonen

2013

IEEE Trans. Signal Process.

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Abstract-Linearly constrained minimum variance (LCMV) beamforming is a popular spatial filtering technique for signal estimation or signal enhancement in many different fields. We consider distributed LCMV (D-LCMV) beamforming in wireless sensor networks (WSNs) with either a fully connected or a tree topology. In the D-LCMV beamformer algorithm, each node fuses its multiple sensor signals into a single-channel signal of which observations are then transmitted to other nodes. We envisage an adaptive/time-recursive implementation where each node adapts its local LCMV beamformer to changes in the local sensor signal statistics, as well as to changes in the statistics of the wirelessly received signals. Although the per-node signal transmission and computational power is greatly reduced compared to a centralized realization, we show that it is possible for each node to generate the centralized LCMV beamformer output as if it had access to all sensor signals in the entire network, without an explicit computation of the network-wide sensor signal covariance matrix. We provide sufficient conditions for convergence and optimality of the D-LCMV beamformer. The theoretical results are validated by means of Monte-Carlo simulations, which demonstrate the performance of the D-LCMV beamformer.

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Clustered Blind Beamforming From Ad-Hoc Microphone Arrays

Cited by 59 publications

References 25 publications

Distributed microphone array processing for speech source separation with classifier fusion

Distributed microphone array processing for speech source separation with classifier fusion

Applications and trends in wireless acoustic sensor networks: A signal processing perspective

Distributed LCMV Beamforming in a Wireless Sensor Network With Single-Channel Per-Node Signal Transmission

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