Deep Ranking-Based Sound Source Localization

Opochinsky, Renana; Laufer-Goldshtein, Bracha; Gannot, Sharon; Chechik, Gal

doi:10.1109/waspaa.2019.8937159

Cited by 22 publications

(15 citation statements)

References 26 publications

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“…For example, in Ref. 117, a weakly-labeled ML paradigm is presented. The approach used few labeled samples with known positions along with larger set of unlabeled samples, for which only their relative physical ordering is known.…”

Section: Speaker Localization In Reverberant Envi-ronmentsmentioning

confidence: 99%

Machine learning in acoustics: Theory and applications

Bianco

Gerstoft

Traer

et al. 2019

The Journal of the Acoustical Society of America

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424

126

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Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science. We survey the recent advances and transformative potential of machine learning (ML), including deep learning, in the field of acoustics. ML is a broad family of statistical techniques for automatically detecting and utilizing patterns in data. Relative to conventional acoustics and signal processing, ML is data-driven. Given sufficient training data, ML can discover complex relationships between features and desired labels or actions, or between features themselves. With large volumes of training data, ML can discover models describing complex acoustic phenomena such as human speech and reverberation. ML in acoustics is rapidly developing with compelling results and significant future promise. We first introduce ML, then highlight ML developments in five acoustics research areas: source localization in speech processing, source localization in ocean acoustics, bioacoustics, seismic exploration, and environmental sounds in everyday scenes.

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Section: Speaker Localization In Reverberant Envi-ronmentsmentioning

confidence: 99%

Machine learning in acoustics: Theory and applications

Bianco

Gerstoft

Traer

et al. 2019

The Journal of the Acoustical Society of America

Self Cite

424

126

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“…A 3D Kalman method was modified to perform 3D localization of sound sources. Learning based sound source localization has been explored by Opochinsky [55]. First, sound attributes are first taken as input from the captured signals.…”

Section: Related Workmentioning

confidence: 99%

Sound Localization for Ad-Hoc Microphone Arrays

et al. 2021

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Sound localization is a field of signal processing that deals with identifying the origin of a detected sound signal. This involves determining the direction and distance of the source of the sound. Some useful applications of this phenomenon exists in speech enhancement, communication, radars and in the medical field as well. The experimental arrangement requires the use of microphone arrays which record the sound signal. Some methods involve using ad-hoc arrays of microphones because of their demonstrated advantages over other arrays. In this research project, the existing sound localization methods have been explored to analyze the advantages and disadvantages of each method. A novel sound localization routine has been formulated which uses both the direction of arrival (DOA) of the sound signal along with the location estimation in three-dimensional space to precisely locate a sound source. The experimental arrangement consists of four microphones and a single sound source. Previously, sound source has been localized using six or more microphones. The precision of sound localization has been demonstrated to increase with the use of more microphones. In this research, however, we minimized the use of microphones to reduce the complexity of the algorithm and the computation time as well. The method results in novelty in the field of sound source localization by using less resources and providing results that are at par with the more complex methods requiring more microphones and additional tools to locate the sound source. The average accuracy of the system is found to be 96.77% with an error factor of 3.8%.

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“…Since the sound source localization needs to map the input features to a metric coordinates or spatial classes that represent the metric coordinates of the sounds source(s), unsupervised learning methods are not commonly used, as it is impossible for a machine learning algorithm to learn the accurate mapping between the feature space and the physical space without any supervision. Although there are some investigations in semi-supervised (Bianco et al 2020;Moing et al 2021;Takeda, Komatani 2017) or weakly-supervised learning strategies (Opochinsky et al 2019), nevertheless, most often, a supervised learning strategy is employed.…”

Section: Learning-based Sound Source Localizationmentioning

confidence: 99%

Improvement of learning-based methods for localization of multiple sound sources

Sakavicius¹

2021

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Deep Ranking-Based Sound Source Localization

Cited by 22 publications

References 26 publications

Machine learning in acoustics: Theory and applications

Machine learning in acoustics: Theory and applications

Sound Localization for Ad-Hoc Microphone Arrays

Improvement of learning-based methods for localization of multiple sound sources

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