Blind compensation of interchannel sampling frequency mismatch for ad hoc microphone array based on maximum likelihood estimation

Miyabe, Shigeki; Ono, Nobutaka; Makino, Shoji

doi:10.1016/j.sigpro.2014.09.015

Cited by 54 publications

(52 citation statements)

References 15 publications

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“…One method of demonstrating the suitability of this proposal consists of studying whether the value of Δ 0 m is close to Δ m (8). In this sense, if we suppose that k m ½s is placed approximately in the value corresponding to the difference in the delays of the s-th source from the reference mixture and the m-th mixture so that k m ½s Cδ 1s À δ ms , then…”

Section: Cc-based Methods For Delay Estimationmentioning

confidence: 99%

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Synchronization based on mixture alignment for sound source separation in wireless acoustic sensor networks

et al. 2016

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Section: Cc-based Methods For Delay Estimationmentioning

confidence: 99%

“…(23), the proposed values of Δ ‴ m will ideally be placed close to the values defined by Eq. (8). For illustrative purposes, Fig.…”

Section: Proposed Methods For Efficient Data Transmissionmentioning

confidence: 99%

Synchronization based on mixture alignment for sound source separation in wireless acoustic sensor networks

et al. 2016

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“…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. A gating procedure is applied to remove the outliers of the TDOA estimation in all frames.…”

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%

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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“…Recently, research on speech enhancement using socalled acoustic sensor networks consisting of spatially distributed microphones has gained significant interest [1][2][3][4][5][6][7][8][9][10][11][12]. Compared with a microphone array at a single position, spatially distributed microphones are able to acquire more information about the sound field.…”

Section: Introductionmentioning

confidence: 99%

Phase reference for the generalized multichannel Wiener filter

Grimm

Lawin-Ore

Doclo

et al. 2016

EURASIP J. Adv. Signal Process.

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The multichannel Wiener filter (MWF) is a well-established noise reduction technique for speech processing. Most commonly, the speech component in a selected reference microphone is estimated. The choice of this reference microphone influences the broadband output signal-to-noise ratio (SNR) as well as the speech distortion. Recently, a generalized formulation for the MWF (G-MWF) was proposed that uses a weighted sum of the individual transfer functions from the speaker to the microphones to form a better speech reference resulting in an improved broadband output SNR. For the MWF, the influence of the phase reference is often neglected, because it has no impact on the narrow-band output SNR. The G-MWF allows an arbitrary choice of the phase reference especially in the context of spatially distributed microphones. In this work, we demonstrate that the phase reference determines the overall transfer function and hence has an impact on both the speech distortion and the broadband output SNR. We propose two speech references that achieve a better signal-to-reverberation ratio (SRR) and an improvement in the broadband output SNR. Both proposed references are based on the phase of a delay-and-sum beamformer. Hence, the time-difference-of-arrival (TDOA) of the speech source is required to align the signals. The different techniques are compared in terms of SRR and SNR performance.

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Blind compensation of interchannel sampling frequency mismatch for ad hoc microphone array based on maximum likelihood estimation

Cited by 54 publications

References 15 publications

Synchronization based on mixture alignment for sound source separation in wireless acoustic sensor networks

Synchronization based on mixture alignment for sound source separation in wireless acoustic sensor networks

Audio Fingerprinting for Multi-Device Self-Localization

Phase reference for the generalized multichannel Wiener filter

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