Distributed max-SINR speech enhancement with ad hoc microphone arrays

Tavakoli, Vincent Mohammad; Jensen, Jesper Rindom; Heusdens, Richard; Benesty, Jacob; Christensen, Mads Græsbøll

doi:10.1109/icassp.2017.7952136

Cited by 14 publications

(7 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DM systems enable the high-quality broadcasting and recording of meetings by utilizing the flexible spatial coverage of ad hoc arrays. Distributed meetings using signal processing techniques are not necessarily online meetings, but could be group meetings [20]. Ad hoc arrays are more suitable recording tools for distributed meetings than are compact microphone arrays, as the recording devices can be spread out in the meeting room.…”

Section: Improved Distributed Meetingsmentioning

confidence: 99%

See 1 more Smart Citation

Distributed Microphone Arrays, Emerging Speech and Audio Signal Processing Platforms: A Review

Pasha¹,

Lundgren²,

Ritz³

et al. 2020

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

Given ubiquitous digital devices with recording capability, distributed microphone arrays are emerging recording tools for hands-free communications and spontaneous tele-conferencings. However, the analysis of signals recorded with diverse sampling rates, time delays, and qualities by distributed microphone arrays is not straightforward and entails important considerations. The crucial challenges include the unknown/changeable geometry of distributed arrays, asynchronous recording, sampling rate mismatch, and gain inconsistency. Researchers have recently proposed solutions to these problems for applications such as source localization and dereverberation, though there is less literature on real-time practical issues. This article reviews recent research on distributed signal processing techniques and applications. New applications benefitting from the wide coverage of distributed microphones are reviewed and their limitations are discussed. This survey does not cover partially or fully connected wireless acoustic sensor networks.

show abstract

Section: Improved Distributed Meetingsmentioning

confidence: 99%

“…An optimization method for the MVDR beamformer using the pseudo-coherence model of the array (24), based on the coherence function (20), is proposed and successfully tested by Tavakoli et al [5]:…”

Section: Minimum Variance Distortionless Response Beamforming (Mvdr)mentioning

confidence: 99%

Distributed Microphone Arrays, Emerging Speech and Audio Signal Processing Platforms: A Review

Pasha¹,

Lundgren²,

Ritz³

et al. 2020

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

show abstract

“…In comparison, the ad-hoc-based enhancement system, however, enhances the noisy input locally in the individual device and then shares the result with its neighbors for further refinement. Some well-known techniques of this type include distributed linearly constrained minimum variance [23], linearly constrained distributed adaptive node-specific signal estimation [24], distributed generalized sidelobe canceler [25], and distributed maximum signal to interference-plus-noise filtering [26].…”

Section: Introductionmentioning

confidence: 99%

Distributed Microphone Speech Enhancement based on Deep Learning

Wang

Liang²,

Hung

et al. 2019

Preprint

View full text Add to dashboard Cite

Speech-related applications deliver inferior performance in complex noise environments. Therefore, this study primarily addresses this problem by introducing speech-enhancement (SE) systems based on deep neural networks (DNNs) applied to a distributed microphone architecture. The first system constructs a DNN model for each microphone to enhance the recorded noisy speech signal, and the second system combines all the noisy recordings into a large feature structure that is then enhanced through a DNN model. As for the third system, a channel-dependent DNN is first used to enhance the corresponding noisy input, and all the channel-wise enhanced outputs are fed into a DNN fusion model to construct a nearly clean signal. All the three DNN SE systems are operated in the acoustic frequency domain of speech signals in a diffuse-noise field environment. Evaluation experiments were conducted on the Taiwan Mandarin Hearing in Noise Test (TMHINT) database, and the results indicate that all the three DNN-based SE systems provide the original noise-corrupted signals with improved speech quality and intelligibility, whereas the third system delivers the highest signal-to-noise ratio (SNR) improvement and optimal speech intelligibility.

show abstract

“…Compared to the traditional microphone arrays, WASNs are more flexible and scalable, and are able to physically cover a larger space and capture more spatial information. The distributed speech enhancement methods, such as the distributed Wiener filter [1], the distributed maximum SNR filter [2], the distributed beamforming [3], need an estimate of the second-order statistics of the noise before forming the linear filter. Usually, the noise covariance matrix is estimated in a recursive way, and the estimated covariance matrix is updated only when the speech is absent.…”

Section: Introductionmentioning

confidence: 99%