Enhanced diffuse field model for ad hoc microphone array calibration

Taghizadeh, Mohammad Javad; Garner, Philip N.; Bourlard, Hervé

doi:10.1016/j.sigpro.2014.02.012

Cited by 14 publications

(14 citation statements)

References 30 publications

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“…In addition, the method proposed in [34,35] is implemented and used to find the location of the circular array. This method relies on diffuse noise model to find the topology of the array.…”

Section: B Source-sensor Localization Performancementioning

confidence: 99%

Binary Sparse Coding of Convolutive Mixtures for Sound Localization and Separation via Spatialization

Asaei

Taghizadeh

Haghighatshoar

et al. 2016

IEEE Trans. Signal Process.

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Abstract-We propose a sparse coding approach to address the problem of source-sensor localization and speech reconstruction. This approach relies on designing a dictionary of spatialized signals by projecting the microphone array recordings into the array manifolds characterized for different locations in a reverberant enclosure using the image model. Sparse representation over this dictionary enables identifying the subspace of the actual recordings and its correspondence to the source and sensor locations. The speech signal is reconstructed by inverse filtering the acoustic channels associated to the array manifolds. We provide rigorous analysis on the optimality of speech reconstruction by elucidating the links between inverse filtering and source separation followed by deconvolution. This procedure is evaluated for localization, reconstruction and recognition of simultaneous speech sources using real data recordings. The results demonstrate the effectiveness of the proposed approach and compare favorably against beamforming and independent component analysis techniques.

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Section: B Source-sensor Localization Performancementioning

confidence: 99%

Binary Sparse Coding of Convolutive Mixtures for Sound Localization and Separation via Spatialization

Asaei

Taghizadeh

Haghighatshoar

et al. 2016

IEEE Trans. Signal Process.

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“…An approach matches the measured noise coherence to the theoretical model of the sound field for estimating the inter-device distances [47], [48]. This approach is only applicable to relatively small arrays and the assumption of a diffuse noise field is not always met in practical applications.…”

Section: Related Workmentioning

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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“…The level of noise in extracting the pairwise distances, w i j in (4), increases as the distances become larger [28]. We model this effect through…”

Section: Noise Modelmentioning

confidence: 99%

Ad hoc microphone array calibration: Euclidean distance matrix completion algorithm and theoretical guarantees

et al. 2015

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This paper addresses the problem of ad hoc microphone array calibration where only partial information about the distances between microphones is available. We construct a matrix consisting of the pairwise distances and propose to estimate the missing entries based on a novel Euclidean distance matrix completion algorithm by alternative low-rank matrix completion and projection onto the Euclidean distance space. This approach confines the recovered matrix to the EDM cone at each iteration of the matrix completion algorithm. The theoretical guarantees of the calibration performance are obtained considering the random and locally structured missing entries as well as the measurement noise on the known distances. This study elucidates the links between the calibration error and the number of microphones along with the noise level and the ratio of missing distances. Thorough experiments on real data recordings and simulated setups are conducted to demonstrate these theoretical insights. A significant improvement is achieved by the proposed Euclidean distance matrix completion algorithm over the state-of-the-art techniques for ad hoc microphone array calibration.

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Enhanced diffuse field model for ad hoc microphone array calibration

Cited by 14 publications

References 30 publications

Binary Sparse Coding of Convolutive Mixtures for Sound Localization and Separation via Spatialization

Binary Sparse Coding of Convolutive Mixtures for Sound Localization and Separation via Spatialization

Audio Fingerprinting for Multi-Device Self-Localization

Ad hoc microphone array calibration: Euclidean distance matrix completion algorithm and theoretical guarantees

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