Proceedings Article10.1109/WASPAA.2013.6701829
Relative transfer function modeling for supervised source localization
Bracha Laufer,Ronen Talmon,Sharon Gannot +2 more
- 01 Oct 2013
- pp 1-4
TL;DR: This work presents a training-based algorithm, motivated by the concept of diffusion maps, that aims at recovering the fundamental controlling parameters driving the measurements, and turns out to be more robust to reverberation, and capable of recovering the speech source location using merely two microphones signals.
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Abstract: Speaker localization is one of the most prevalent problems in speech processing. Despite significant efforts in the last decades, high reverberation level still limits the performance of localization algorithms. Furthermore, using conventional localization methods, the information that can be extracted from dual microphone measurements is restricted to the time difference of arrival (TDOA). Under far-field regime, this is equivalent to either azimuth or elevation angles estimation. Full description of speaker's coordinates necessitates several microphones. In this contribution we tackle these two limitations by taking a manifold learning perspective for system identification. We present a training-based algorithm, motivated by the concept of diffusion maps, that aims at recovering the fundamental controlling parameters driving the measurements. This approach turns out to be more robust to reverberation, and capable of recovering the speech source location using merely two microphones signals.
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Citations
Machine learning in acoustics: Theory and applications
Michael J. Bianco,Peter Gerstoft,James Traer,Emma Ozanich,Marie A. Roch,Sharon Gannot,Charles-Alban Deledalle +6 more
TL;DR: This work surveys the recent advances and transformative potential of machine learning (ML), including deep learning, in the field of acoustics, and highlights ML developments in four acoustICS research areas: source localization in speech processing, source localized in ocean acoustic, bioacoustics and environmental sounds in everyday scenes.
321
Machine learning in acoustics: theory and applications
Michael J. Bianco,Peter Gerstoft,James Traer,Emma Ozanich,Marie A. Roch,Sharon Gannot,Charles-Alban Deledalle +6 more
TL;DR: In this paper, the authors survey the recent advances and transformative potential of machine learning (ML) including deep learning, in the field of acoustics and highlight ML developments in four acoustICS research areas: source localization in speech processing, source localization from ocean acoustic, bioacoustics, and environmental sounds in everyday scenes.
229
Semi-supervised sound source localization based on manifold regularization
TL;DR: In this article, a semi-supervised source localization algorithm based on two-microphone measurements is proposed, which recovers the inverse mapping between the acoustic samples and their corresponding locations.
79
Multiple Source Direction of Arrival Estimations Using Relative Sound Pressure Based MUSIC
TL;DR: A novel MUSIC algorithm is developed, more suitable in noisy environments, using the relative sound pressure measurements of a higher order microphone array, and decomposed into the spherical harmonics domain where a frequency smoothing technique is allowed to de-correlate the coherent source signals for improved localization accuracy.
56
A discriminative learning approach to probabilistic acoustic source localization
Hendrik Kayser,Jörn Anemüller +1 more
- 20 Nov 2014
TL;DR: A classification-based method for source localization that uses discriminative support vector machine-learning of correlation patterns that are indicative of source presence or absence that generates a map of sound source presence probability in given directions is presented.
51
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