On the importance of exact synchronization for distributed audio signal processing

Lienhart, Rainer; Kozintsev, I.; Yeung, Minerva M.; Wehr, Stefan

doi:10.1109/icassp.2003.1202774

Cited by 41 publications

(33 citation statements)

References 5 publications

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“…Digital signal processing tools that take audio from multiple different transducers as their input, such as blind source separation (BSS) and acoustic echo cancellation (AEC), will not work as expected if these audio streams are not synchronized [2]. To use these tools with audio streams generated by distributed devices it is necessary to correct the mismatches in sampling rate.…”

Section: Introductionmentioning

confidence: 98%

“…The necessary resampling factors may take arbitrary values and may change with time, so a traditional resampling approach using cascaded decimators and interpolators is not practical for this. Instead, interpolation filters are typically used [4,2,3]. This still presents computational challenges, due to the need of a different interpolation filter for each output sample.…”

Section: Introductionmentioning

confidence: 99%

“…Regarding the estimation of the rate mismatch, one approach described in [2] is to transmit some known calibration sound signal to all the devices. In order to avoid interference with the actual application this needs to be performed through a quiet channel, such as an FM radio receiver, available to all the devices.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

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 first problem can be dealt with by connecting the microphones to much cheaper single-channel A/D converters, however the result is desynchronization of the recorded signals. Lienhart et al proposed to synchronize the recording devices over a network [9]. Another solution has been proposed by Ono et al, who developed a method to jointly estimate the microphone locations, the single source location and the time origins of the recording devices [8,10].…”

Section: Introductionmentioning

confidence: 99%

Distributed acoustic slam

Grzymkowski

Glowczewski

Raczynski

2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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Vision-based methods are very popular for simultaneous localization and environment mapping (SLAM). One can imagine that exploiting the natural acoustic landscape of the robot's environment can prove to be a useful alternative to vision SLAM. Visual SLAM depends on matching local features between images, whereas distributed acoustic SLAM is based on matching acoustic events. Proposed DASLAM is based on distributed microphone arrays, where each microphone is connected to a separate, moving, controllable recording device, which requires compensation for their different clock shifts. We show that this controlled mobility is necessary to deal with underdetermined cases. Estimation is done using particle filtering.Results show that both tasks can be accomplished with good precision, even for the theoretically underdetermined cases. For example, we were able to achieve mapping error as low as 17.53 cm for sound sources with localization error of 18.61 cm and clock synchronization error of 42 μs for 2 robots and 2 sources.

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“…al. [4] developed a system to synchronize the audio signals by having the individual microphone devices to send special synchronization signals over a dedicated link. Raykar et.…”

Section: Introductionmentioning

confidence: 99%

Energy-Based Position Estimation of Microphones and Speakers for Ad Hoc Microphone Arrays

Chen

Liu

et al. 2007

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

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We present a novel energy-based algorithm to estimate the positions of microphones and speakers in an ad hoc microphone array setting. Compared to traditional time-of-flight based approaches, energy-based approach has the advantage that it does not require accurate time synchronization. This property is particularly useful for ad hoc microphone arrays because highly accurate synchronization across microphones may be difficult to obtain since these microphones usually belong to different devices. This new algorithm extends our previous energy-based position estimation algorithm [1] in that it does not assume the speakers are in the same positions as their corresponding microphones. In fact, our new algorithm estimates both the microphones and speakers simultaneously. Experiment results are shown to demonstrate its performance improvement over the previous approach in [1], and evaluate its robustness against time synchronization errors.

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On the importance of exact synchronization for distributed audio signal processing

Cited by 41 publications

References 5 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 acoustic slam

Energy-Based Position Estimation of Microphones and Speakers for Ad Hoc Microphone Arrays

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