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

Markovich‐Golan, Shmulik; Gannot, Sharon; Cohen, Israel

doi:10.1109/tasl.2012.2224454

Cited by 58 publications

(69 citation statements)

References 28 publications

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“…Subsection 3.1 focuses on the iterative convergence of DMV using PDMM while subsection 3.2 compares the performance of acyclic DMV with other state of the art algorithms, in particular distributed LCMV (DLCMV) [1] and distributed GSC (DGSC) [11].…”

Section: Dmv Performance In Different Network Topologiesmentioning

confidence: 99%

“…In contrast, the BFs in [1,11] choose to restrict the WSN networks to be either fully connected or acyclic (tree shaped). By exploiting the ease of data aggregation in such networks, these algorithms allow for the combination of local BFs based on linearly constrained minimum variance (LCMV) [1] and generalised sidelobe canceller (GSC) [11] topologies, to approximate a global BF response.…”

Section: Introductionmentioning

confidence: 99%

“…By exploiting the ease of data aggregation in such networks, these algorithms allow for the combination of local BFs based on linearly constrained minimum variance (LCMV) [1] and generalised sidelobe canceller (GSC) [11] topologies, to approximate a global BF response. Both algorithms achieve fully scalable distributed implementations which, in static noise fields, converge to the optimal BF over multiple updates.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the BF computation can be performed via a range of existing distributed tools [2,13,16,21,17] for both cyclic and acyclic networks. This flexibility makes it possible to use the proposed algorithm for optimal beamforming without the impractical restriction of enforcing tree-shaped networks, as required in [1,11].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Room geometry estimation from acoustic echoes using graph-based echo labeling

Jager

Heusdens

Gaubitch

2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper we propose a distributed reformulation of the linearly constrained minimum variance (LCMV) beamformer for use in acoustic wireless sensor networks. The proposed distributed minimum variance (DMV) algorithm, for which we demonstrate implementations for both cyclic and acyclic networks, allows the optimal beamformer output to be computed at each node without the need for sharing raw data within the network. By exploiting the low rank structure of estimated covariance matrices in time-varying noise fields, the algorithm can also provide a reduction in the total amount of data transmitted during computation when compared to centralised solutions. This is particularly true when multiple microphones are used per node. We also compare the performance of DMV with state of the art distributed beamformers and demonstrate that it achieves greater improvements in SNR in dynamic noise fields with similar transmission costs.

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Section: Dmv Performance In Different Network Topologiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Room geometry estimation from acoustic echoes using graph-based echo labeling

Jager

Heusdens

Gaubitch

2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…However, due to privacy considerations, transmission range and battery limitations, such a fusion center may be undesirable in many applications. An alternative solution is to use distributed noise reduction algorithms, e.g., [10]- [14], where each node can process data locally and communicate with its neighbors, rather than with a fusion center.…”

Section: Introductionmentioning

confidence: 99%

Distributed delay and sum beamformer for speech enhancement in wireless sensor networks via randomized gossip

Zeng

Hendriks

2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Abstract-In this paper, we investigate the use of randomized gossip for distributed speech enhancement and present a distributed delay and sum beamformer (DDSB). In a randomly connected wireless acoustic sensor network, the DDSB estimates the desired signal at each node by communicating only with its neighbors. We first provide the asynchronous DDSB (ADDSB) where each pair of neighboring nodes updates its data asynchronously. Then, we introduce an improved general distributed synchronous averaging (IGDSA) algorithm, which can be used in any connected network, and combine that with the DDSB algorithm where multiple node pairs can update their estimates simultaneously. For convergence analysis, we first provide bounds for the worst case averaging time of the ADDSB for the best and worst connected networks, and then we compare the convergence rate of the ADDSB with the original synchronous DDSB (OSDDSB) and the improved synchronous DDSB (ISDDSB) in regular networks. This convergence rate comparison is extended to randomly connected non-regular networks using simulations. The simulation results show that the DDSB using the different updating schemes converges to the optimal estimates of the centralized beamformer and that the proposed IGDSA algorithm converges much faster than the original synchronous communication scheme, in particular for non-regular networks. Moreover, comparisons are performed with several existing distributed speech enhancement methods from literature, assuming that the steering vector is given. In the simulated scenario, the proposed method leads to a slight performance improvement at the expense of a higher communication cost. The presented method is not constrained to a certain network topology (e.g., tree connected or fully connected), while this is the case for many of the reference methods.Index Terms-Distributed delay and sum beamformer, randomized gossip, speech enhancement, wireless acoustic sensor networks.

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Distributed multichannel Wiener filtering for speech enhancement in acoustic sensor networks

Chang

Chen

Yin

2022

Adaptive Control & Signal

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Summary The performance of speech processing algorithms in wireless acoustic sensor networks (WASNs) is severely degraded by diffuse noise. To address this problem, a distributed speech enhancement algorithm for WASNs is proposed in this article. The parameterized multichannel Wiener filter is first improved by exploiting all cross‐correlation information among nodes. Next, using the Hadamard element‐wise product of label matrix and cross‐correlation matrix, the distributed parameterized multichannel Wiener filter based on the in‐network summation is presented. Moreover, by making the tradeoff between communication cost and enhancement performance, a simplified distributed algorithm is proposed. Finally, the noise reduction performance of the proposed method is theoretically analyzed. The proposed method achieves good speech enhancement performance and requires only in‐network summation in WASNs. Experimental results reveal the validity of the proposed method.

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

Cited by 58 publications

References 28 publications

Room geometry estimation from acoustic echoes using graph-based echo labeling

Room geometry estimation from acoustic echoes using graph-based echo labeling

Distributed delay and sum beamformer for speech enhancement in wireless sensor networks via randomized gossip

Distributed multichannel Wiener filtering for speech enhancement in acoustic sensor networks

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