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36
‘Shadow BSS ’ for Blind Source Separation in Rapidly Time-Varying Acoustic Scenes
"... Abstract. This paper addresses the tracking capability of blind source separation algorithms for rapidly time-varying sensor or source positions. Based on a known algorithm for blind source separation, which also allows for simultaneous localization of multiple active sources in reverberant environm ..."
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Abstract. This paper addresses the tracking capability of blind source separation algorithms for rapidly time-varying sensor or source positions. Based on a known algorithm for blind source separation, which also allows for simultaneous localization of multiple active sources in reverberant environments, the source separation performance will be investigated for abrupt microphone array rotations representing the worst case. After illustrating the deficiencies in source-tracking with the given efficient implementation of the BSS algorithm, a method to ensure robust source separation even with abrupt microphone array rotations is proposed. Experimental results illustrate the efficiency of the proposed concept. 1
Tracking of two acoustic sources in reverberant environments using a particle swarm optimizer
- In IEEE Conference on Advanced Video and Signal Based Surveillance
, 2007
"... Abstract In this paper we consider the problem of tracking multiple acoustic sources in reverberant environments. The solution that we propose is based on the combination of two techniques. A blind source separation (BSS) method known as TRINICON [5] is applied to the signals acquired by the micro ..."
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Abstract In this paper we consider the problem of tracking multiple acoustic sources in reverberant environments. The solution that we propose is based on the combination of two techniques. A blind source separation (BSS) method known as TRINICON [5] is applied to the signals acquired by the microphone arrays. The TRINICON de-mixing filters are used to obtain the Time Differences of Arrival (TDOAs), which are related to the source location through a nonlinear function. A particle filter is then applied in order to localize the sources. Particles move according to a swarm-like dynamics, which significatively reduces the number of particles involved with respect to traditional particle filter. We discuss results for the case of two sources and four microphone pairs. In addition, we propose a method, based on detecting source inactivity, which overcomes the ambiguities that intrinsically arise when only two microphone pairs are used. Experimental results demonstrate that the average localization error on a variety of pseudo-random trajectories is around 40cm when the T 60 reverberation time is 0.6s.
Sparsity-aware TDOA localization of multiple sources
- in Proc. IEEE Intl. Conf. Acoust., Speech, Signal Process. (ICASSP
, 2013
"... 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 ..."
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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 clas-sical least squares (LS) approaches. Our simulation results illustrate a good performance for the introduced TDOA fingerprinting. Index Terms — Multi-source localization, TDOA fingerprint-ing, sparse reconstruction. 1.
The SCENIC Project: Space-Time Audio Processing for Environment-Aware Acoustic Sensing and Rendering
, 2011
"... This Convention paper was selected based on a submitted abstract and 750-word precis that have been peer reviewed by at least two qualified anonymous reviewers. The complete manuscript was not peer reviewed. This convention paper has been reproduced from the author's advance manuscript without ..."
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This Convention paper was selected based on a submitted abstract and 750-word precis that have been peer reviewed by at least two qualified anonymous reviewers. The complete manuscript was not peer reviewed. This convention paper has been reproduced from the author's advance manuscript without editing, corrections, or consideration by the Review Board. The AES takes no responsibility for the contents. Additional papers may be obtained by sending request and remittance to Audio
EFPC: An Environmentally Friendly Power Control Scheme for Underwater Sensor Networks
- SENSORS
, 2015
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ATwo-ChannelAcousticFront-EndforRobustAutomaticSpeech Recognition in Noisy and Reverberant Environments
"... An acoustic front-end for robust automatic speech recognition in noisy and reverberant environments is proposed in this contribution. It comprises a blind source separation-based signal extraction scheme and only requires two microphone signals. The proposed front-end and its integration into the re ..."
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An acoustic front-end for robust automatic speech recognition in noisy and reverberant environments is proposed in this contribution. It comprises a blind source separation-based signal extraction scheme and only requires two microphone signals. The proposed front-end and its integration into the recognition system is analyzed and evaluated in noisy living room-like environments according to the PASCAL CHiME challenge. The results show that the introduced system significantly improves the recognition performance compared to the challenge baseline. Index Terms: PASCALCHiMEchallenge,robustautomatic speech recognition, blind source extraction, speech enhancement 1.
Contents 4 TRINICON-based Blind System Identification with Application to Multiple-Source Localization and Separation.
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Processing for Environment-Aware Acoustic Sensing and Rendering
"... This paper was peer-reviewed as a complete manuscript for presentation at this Convention. Additional papers may be obtained ..."
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This paper was peer-reviewed as a complete manuscript for presentation at this Convention. Additional papers may be obtained