Blind Source Separation of Acoustic Mixtures with Distributed Microphones

Robledo-Arnuncio, Enrique; Juang, Biing‐Hwang

doi:10.1109/icassp.2007.366838

Cited by 11 publications

(7 citation statements)

References 9 publications

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“…If ICA succeeds, the resulting outputs will relate to the original sources through an arbitrary permutation and arbitrary gains, due to the scaling and permutation ambiguities inherent in ICA. The permutation ambiguity is resolved using the approach described in [6].…”

Section: Blind Source Separationmentioning

confidence: 99%

“…NLMS was ran for 30 seconds, and the AEC performance was measured in terms of the echo return loss enhancement (ERLE) ERLE ≡ 10 log 10 P N n d 2 (n) P N n e 2 (n) (dB) (6) by taking the last one-third of the data for a sufficient filter convergence. The impulse response was scaled to produce the echo return loss (ERL) of 10 dB, and white Gaussian noise was added to d(n) to produce 40 dB signal-to-noise ratio (SNR).…”

Section: Acoustic Echo Cancellation Setupmentioning

confidence: 99%

See 1 more Smart Citation

On Dealing with Sampling Rate Mismatches in Blind Source Separation and Acoustic Echo Cancellation

Robledo-Arnuncio

Wada

Juang

2007

2007 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics

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The lack of a common clock reference is a fundamental problem when dealing with audio streams originating from or heading to different distributed sound capture or playback devices. When implementing multichannel signal processing algorithms for such kind of audio streams it is necessary to account for the unavoidable mismatches between the actual sampling rates. There are some approaches that can help to correct these mismatches, but important problems remain to be solved, among them the accurate estimation of the mismatch factors, and achieving both accuracy and computational efficiency in their correction. In this paper we present an empirical study on the performance of blind source separation and acoustic echo cancellation algorithms in this scenario. We also analyze the degradation in performance when using an approximate but efficient method to correct the rate mismatches.

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Section: Blind Source Separationmentioning

confidence: 99%

Section: Acoustic Echo Cancellation Setupmentioning

confidence: 99%

On Dealing with Sampling Rate Mismatches in Blind Source Separation and Acoustic Echo Cancellation

Robledo-Arnuncio

Wada

Juang

2007

2007 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics

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“…In centralized approaches, e.g., [3,4], the recordings of all sensors (microphones) are transmitted to a centralized processor, the fusion center, to perform the separation procedure. Drawbacks of the centralized approach include the lack of scalability, high power consumption of the transmission and the need for hardware that can transmit over long distances [5].…”

Section: Introductionmentioning

confidence: 99%

Distributed linear blind source separation over wireless sensor networks with arbitrary connectivity patterns

Alavi

Kleijn

2016

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

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Broad areal coverage and low cost make wireless sensor networks natural platforms for blind source separation (BSS). In this context, distributed processing is attractive because of low power requirements and scalability. However, existing distributed BSS algorithms either require a fully connected pattern of connectivity or require a high computational load at each sensor node. We introduce a distributed robust BSS algorithm that uses a fully shared computation and can be applied over any connected graph. This enables us to facilitate a low computational load at each node as well as low data transmission rates. Comparative experimental results confirm the effectiveness of the new method.

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“…It is thought of creating a multipleinput multiple-output system, where each output only includes the speech of the dedicated speaker. Similar techniques can be introduced by blind source separation algorithms [12,13]. These methods are often computationally more expensive.…”

Section: Introductionmentioning

confidence: 99%

A dynamic multi-channel speech enhancement system for distributed microphones in a car environment

Matheja¹,

Buck²,

Fingscheidt

2013

EURASIP J. Adv. Signal Process.

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Supporting multiple active speakers in automotive hands-free or speech dialog applications is an interesting issue not least due to comfort reasons. Therefore, a multi-channel system for enhancement of speech signals captured by distributed distant microphones in a car environment is presented. Each of the potential speakers in the car has a dedicated directional microphone close to his position that captures the corresponding speech signal. The aim of the resulting overall system is twofold: On the one hand, a combination of an arbitrary pre-defined subset of speakers' signals can be performed, e.g., to create an output signal in a hands-free telephone conference call for a far-end communication partner. On the other hand, annoying cross-talk components from interfering sound sources occurring in multiple different mixed output signals are to be eliminated, motivated by the possibility of other hands-free applications being active in parallel. The system includes several signal processing stages. A dedicated signal processing block for interfering speaker cancellation attenuates the cross-talk components of undesired speech. Further signal enhancement comprises the reduction of residual cross-talk and background noise. Subsequently, a dynamic signal combination stage merges the processed single-microphone signals to obtain appropriate mixed signals at the system output that may be passed to applications such as telephony or a speech dialog system. Based on signal power ratios between the particular microphone signals, an appropriate speaker activity detection and therewith a robust control mechanism of the whole system is presented. The proposed system may be dynamically configured and has been evaluated for a car setup with four speakers sitting in the car cabin disturbed in various noise conditions.

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Blind Source Separation of Acoustic Mixtures with Distributed Microphones

Cited by 11 publications

References 9 publications

On Dealing with Sampling Rate Mismatches in Blind Source Separation and Acoustic Echo Cancellation

On Dealing with Sampling Rate Mismatches in Blind Source Separation and Acoustic Echo Cancellation

Distributed linear blind source separation over wireless sensor networks with arbitrary connectivity patterns

A dynamic multi-channel speech enhancement system for distributed microphones in a car environment

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