A Framework for Speech Enhancement With Ad Hoc Microphone Arrays

In this work, we present an algorithm to estimate the relative acoustic transfer function (RTF) of a target source in wireless acoustic sensor networks (WASNs). Two well-known methods to estimate the RTF are the covariance subtraction (CS) method and the covariance whitening (CW) approach, the latter based on the generalized eigenvalue decomposition. Both methods depend on the use of the noisy correlation matrix, which, in practice, has to be estimated using limited and (in WASNs) quantized data. The bit-rate and the fact that we use limited data records therefore directly affect the accuracy of the estimated RTFs. Therefore, we first theoretically analyze the estimation performance of the two approaches in terms of bit rate. Secondly, we propose for both RTF estimators a rate-distribution method by minimizing the power usage and constraining the expected estimation error. The optimal rate distributions are found by using convex optimization techniques. The model-based methods, however, are impractical due to the dependence on the true RTFs. We therefore further develop two greedy rate-distribution methods for both approaches. Finally, numerical simulations on synthetic data and real audio recordings show the superiority of the proposed approaches in power usage compared to uniform rate allocation. We find that in order to satisfy the same RTF estimation accuracy, the rate-distributed CW methods consume much less transmission energy than the CS-based methods.

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“…Note that in fact the termσ 2 X1 d H R −1 nn d is the output SNR of an MVDR beamformer [4], [28]- [30]. Remark 1.…”

Section: B Performance Analysis For Cw Methodsmentioning

confidence: 99%

Relative Acoustic Transfer Function Estimation in Wireless Acoustic Sensor Networks

Zhang

Heusdens

Hendriks

2019

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…Tablets, smartphones, sound recorders, and other portable and wearable digital devices are becoming prevalent in workplaces, homes, and lecture halls, redefining how we communicate and record our communications. Consequently, these devices are becoming key tools for daily activities, including teleconferencing and hands-free speech communication [16]; ad hoc signal processing is therefore inevitable.…”

Section: Significance and Applicationsmentioning

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.

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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.

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“…The cost of the algorithm research and the complexity of data processing should be considered when selecting the number of microphones. If too few microphones are selected, the microphone array [8][9][10][11] will not receive enough information, which greatly affects the precision analysis of the localization algorithm, whereas too many microphones will increase the cost and complexity.…”

Section: Three-plane Five-element Microphone Array Modelmentioning

confidence: 99%

“…Moreover, a target sound source can be located by receiving the sound source signal and applying an algorithm. Microphone arrays [8][9][10][11] perform functions such as noise elimination and target tracking. They can also be used to passively receive sound source signals, making it practical for researchers to collect signals.…”

Section: Introductionmentioning

confidence: 99%

Sound Source Localization Fusion Algorithm and Performance Analysis of a Three-Plane Five-Element Microphone Array

Xing

Yang

2019

Applied Sciences

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To reduce the negative effect on sound source localization when the source is at an extreme angle and improve localization precision and stability, a theoretical model of a three-plane five-element microphone array is established, using time-delay values to judge the sound source’s quadrant position. Corresponding judgment criteria were proposed, solving the problem in which a single-plane array easily blurs the measured position. Based on sound source geometric localization, a formula for the sound source azimuth calculation of a single-plane five-element microphone array was derived. The sinusoids and cosines of two elevation angles based on two single-plane arrays were introduced into the sound source spherical coordinates as composite weighted coefficients, and a sound source localization fusion algorithm based on a three-plane five-element microphone array was proposed. The relationship between the time-delay estimation error, elevation angle, horizontal angle, and microphone array localization performance was discussed, and the precision and stability of ranging and direction finding were analyzed. The results show that the measurement precision of the distance from the sound source to the array center and the horizontal angle are improved one to threefold, and the measurement precision of the elevation angle is improved one to twofold. Although there is a small error, the overall performance of the sound source localization is stable, reflecting the advantages of the fusion algorithm.

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A Framework for Speech Enhancement With Ad Hoc Microphone Arrays

Cited by 36 publications

References 39 publications

Relative Acoustic Transfer Function Estimation in Wireless Acoustic Sensor Networks

Relative Acoustic Transfer Function Estimation in Wireless Acoustic Sensor Networks

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

Sound Source Localization Fusion Algorithm and Performance Analysis of a Three-Plane Five-Element Microphone Array

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