A combined hardware–software approach for acoustic sensor network synchronization

Schmalenstroeer, Joerg; Jebramcik, Patrick; Haeb‐Umbach, Reinhold

doi:10.1016/j.sigpro.2014.06.030

Cited by 28 publications

(17 citation statements)

References 26 publications

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“…A frame is flagged as active if its SNR is over a threshold . Next, GCC-PHAT is applied in each active frame to calculate the generalized cross-correlation function between two microphones and : (7) where is the time delay, is the total number of frequency bins in the whole frequency band, and is the frequency at the -th frequency bin. Assuming at most one source is active in the -th frame, its TDOA is estimated as (8) where can be searched in the whole time frame.…”

Section: B Baseline Solution For Extreme Tdoa Estimationmentioning

confidence: 99%

“…Several challenges, such as asynchronous sampling and unknown time offset between devices, arise when localizing (unconnected) devices with sound [1]. Asynchronous sampling can be compensated for in advance with prior knowledge of the smartphones, or using radio signals for synchronizing local clocks [7], [8]. The unknown time offset is mainly due to the unknown processing time of the devices, which causes sending and receiving uncertainties.…”

Section: Introductionmentioning

confidence: 99%

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Audio Fingerprinting for Multi-Device Self-Localization

Hon

Wang

Reiss

et al. 2015

IEEE/ACM Trans. Audio Speech Lang. Process.

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We investigate the self-localization problem of an ad-hoc network of randomly distributed and independent devices in an open-space environment with low reverberation but heavy noise (e.g. smartphones recording videos of an outdoor event). Assuming a sufficient number of sound sources, we estimate the distance between a pair of devices from the extreme (minimum and maximum) time difference of arrivals (TDOAs) from the sources to the pair of devices without knowing the time offset. The obtained inter-device distances are then exploited to derive the geometrical configuration of the network. In particular, we propose a robust audio fingerprinting algorithm for noisy recordings and perform landmark matching to construct a histogram of the TDOAs of multiple sources. The extreme TDOAs can be estimated from this histogram. By using audio fingerprinting features, the proposed algorithm works robustly in very noisy environments. Experiments with free-field simulation and open-space recordings prove the effectiveness of the proposed algorithm.Index Terms-Ad-hoc microphone array, audio fingerprinting, multi-source, self-localization, time difference of arrival (TDOA) estimation.

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Section: B Baseline Solution For Extreme Tdoa Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Audio Fingerprinting for Multi-Device Self-Localization

Hon

Wang

Reiss

et al. 2015

IEEE/ACM Trans. Audio Speech Lang. Process.

View full text Add to dashboard Cite

show abstract

“…There are different strategies to estimate the clock parameters known from literature. In [8], [9], [10], the internal clocks are synchronised to a reference or virtual clock by exchanging a series of time stamps. In [11], [12], the clock parameters are estimated by correlating calibration signals with a known reference signal, while in [13], [14], [15], [16], the parameters are estimated by exchanging the recorded audio signals.…”

Section: Introductionmentioning

confidence: 99%

Clock-Offset and Microphone Gain Mismatch Invariant Beamforming

Kotti

Heusdens

Hendriks

2021

2020 28th European Signal Processing Conference (EUSIPCO)

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“…Even though certain speech separation algorithms do not need perfect synchronization between the sampling times of distributed microphones (e.g. beamforming algorithms [17]- [19] can deal with any systematic delay within the analysis window), sampling rate offsets due to internal clock drift on different network modules (therefore their microphones) will lead to degradation of the algorithm output [20]. Some work has been carried out in the field of blind synchronization of asynchronous recordings [21]- [25].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Multi-Channel Multi-Device Speech Separation Algorithms in the Wild: A Hardware-Software Solution

Ceolini

Kiselev

Liu

2020

IEEE/ACM Trans. Audio Speech Lang. Process.

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Evaluation methods for multi-channel speech separation algorithms in the real world are becoming increasingly important as the number of applications involving audio assistants and hearing aid devices continues to grow. To make such evaluations easier, this paper presents a multi-microphone hardware platform, WHISPER, built specifically for this purpose and its subsequent use for evaluating speech processing algorithms. The platform can also be constructed as an ad-hoc wireless acoustic sensor network (WASN) with high synchronization precision. Using WHISPER, we describe real-world experiments where an example speech separation algorithm is applied to mixtures of varying number of talkers and signal-to-noise ratios. The results when compared with those from a simulated environment, show the usefulness of WASNs and that simulations tend to underestimate the difficulty of speech separation in real-world scenarios. This work represents an important step towards developing a hardware-software framework for evaluating speech processing algorithms in the wild.

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A combined hardware–software approach for acoustic sensor network synchronization

Cited by 28 publications

References 26 publications

Audio Fingerprinting for Multi-Device Self-Localization

Audio Fingerprinting for Multi-Device Self-Localization

Clock-Offset and Microphone Gain Mismatch Invariant Beamforming

Evaluating Multi-Channel Multi-Device Speech Separation Algorithms in the Wild: A Hardware-Software Solution

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