Multidimensional localization of multiple sound sources using averaged directivity patterns of Blind Source Separation systems

Lombard, Anthony; Rosenkranz, Tobias; Buchner, Herbert; Kellermann, Walter

doi:10.1109/icassp.2009.4959563

Cited by 31 publications

(13 citation statements)

References 6 publications

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“…spectives in literature [11,12,13,14,15,16,17,18]. In the speech and acoustic domain, some of these studies consider disjoint sources such as [12] and in many others linear array receivers are assumed and thus the problem basically boils down to direction of arrival (DOA) estimation [15].…”

Section: Introductionmentioning

confidence: 99%

Sparsity-aware TDOA localization of multiple sources

Jamali-Rad

Leus

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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The problem of source localization from time-difference-of-arrival (TDOA) measurements is in general a non-convex and complex problem due to its hyperbolic nature. This problem becomes even more complicated for the case of multi-source localization where TDOAs should be assigned to their respective sources. We simplify this problem to an ℓ1-norm minimization by introducing a novel TDOA fingerprinting model for a multi-source scenario. Moreover, we propose an innovative trick to enhance the performance of our proposed fingerprinting model in terms of the number of identifiable sources. An interesting by-product of this enhanced model is that under some conditions we can convert the given underdetermined problem to an overdetermined one and efficiently solve it using classical least squares (LS) approaches. Our simulation results illustrate a good performance for the introduced TDOA fingerprinting.

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Section: Introductionmentioning

confidence: 99%

Sparsity-aware TDOA localization of multiple sources

Jamali-Rad

Leus

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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“…If the target source position is not known, an additional source localizer is necessary. Many localization algorithms can be used as, e.g., GCC-PHAT [39] or an ICA-based source localizer [40]. With the given DoA information, we can initialize the filter structure corresponding to a DSB in order to accelerate convergence.…”

Section: Algorithmmentioning

confidence: 99%

“…In practical applications, usually the target direction is unknown and needs to be estimated using a source localization algorithm [39,40] so that estimation errors must be expected. Hence, the robustness of Directional BSS against localization errors is of special interest.…”

Section: Robustness Against Localization Errorsmentioning

confidence: 99%

Analysis of dual-channel ICA-based blocking matrix for improved noise estimation

Zheng

Reindl

Kellermann

2014

EURASIP J. Adv. Signal Process.

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For speech enhancement or blind signal extraction (BSE), estimating interference and noise characteristics is decisive for its performance. For multichannel approaches using multiple microphone signals, a BSE scheme combining a blocking matrix (BM) and spectral enhancement filters was proposed in numerous publications. For such schemes, the BM provides a noise estimate by suppressing the target signal only. The estimated noise reference is then used to design spectral enhancement filters for the purpose of noise reduction. For designing the BM, 'Directional Blind Source Separation (BSS)' was already proposed earlier. This method combines a generic BSS algorithm with a geometric constraint derived from prior information on the target source position to obtain an estimate for all interfering point sources and diffuse background noise. In this paper, we provide a theoretical analysis to show that Directional BSS converges to a relative transfer function (RTF)-based BM. The behavior of this informed signal separation scheme is analyzed and the blocking performance of Directional BSS under various acoustical conditions is evaluated. The robustness of Directional BSS regarding the localization error for the target source position is verified by experiments. Finally, a BSE scheme combining Directional BSS and Wiener-type spectral enhancement filters is described and evaluated.

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“…That is why some of these studies consider disjoint sources such as [12] and in many others linear array receivers are assumed and thus the problem basically boils down to direction of arrival (DOA) estimation [15]. In a big line of research, the conversion of phase to TDOA leads to aliasing effects at high frequencies for large receiver spacings [13], [15]. In [13], for instance, a blind source separation (BSS) signal model is considered and a beamforming procedure is proposed to produce an acoustic map of the covered area.…”

mentioning

confidence: 99%

“…In a big line of research, the conversion of phase to TDOA leads to aliasing effects at high frequencies for large receiver spacings [13], [15]. In [13], for instance, a blind source separation (BSS) signal model is considered and a beamforming procedure is proposed to produce an acoustic map of the covered area. To obtain such a map, distance information (between source(s) and receivers) is required which becomes computationally demanding for a near-field assumption.…”

mentioning

confidence: 99%

Sparsity-Aware Multi-Source TDOA Localization

Jamali-Rad

Leus

2013

IEEE Trans. Signal Process.

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Abstract-The problem of source localization from time-difference-of-arrival (TDOA) measurements is in general a non-convex and complex problem due to its hyperbolic nature. This problem becomes even more complicated for the case of multi-source localization where TDOAs should be assigned to their respective sources. We simplify this problem to an -norm minimization by introducing a novel TDOA fingerprinting and grid design model for a multi-source scenario. Moreover, we propose an innovative trick to enhance the performance of our proposed fingerprinting model in terms of the number of identifiable sources. An interesting by-product of this enhanced model is that under some conditions we can convert the given underdetermined problem to an overdetermined one that could be solved using classical least squares (LS). Finally, we also tackle the problem of off-grid source localization as a case of grid mismatch. Our extensive simulation results illustrate a good performance for the introduced TDOA fingerprinting paradigm as well as a significant detection gain for the enhanced model.

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Multidimensional localization of multiple sound sources using averaged directivity patterns of Blind Source Separation systems

Cited by 31 publications

References 6 publications

Sparsity-aware TDOA localization of multiple sources

Sparsity-aware TDOA localization of multiple sources

Analysis of dual-channel ICA-based blocking matrix for improved noise estimation

Sparsity-Aware Multi-Source TDOA Localization

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