Multiple Sound Sources Localization with Frame-by-Frame Component Removal of Statistically Dominant Source

Jia, Maoshen; Wu, Yuxuan; Bao, Changchun; Wang, Jing

doi:10.3390/s18113613

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…The proposed TCDMA-AGGPM algorithm is compared with HiGRID [19], SH-TMSBL [21], SF-MCA [24], and TF-MW-BNP-AHB [25] methods for two and three simultaneous speakers in noisy and reverberant environments on real and simulated data. The mean absolute estimation error (MAEE) [35] criteria is selected for measuring the accuracy and robustness of the proposed TCDMA-AGGPM method in comparison with other previous works. This criteria provides a measurement scale by calculating the accurate distance between 3D estimated speaker's location ( xq , ŷq , ẑq ) and real speaker's location (x q , y q , z q ) with averaging on N t continuous frames of overlapped speech signal, which is expressed as:…”

Section: The Evaluation's Scenariosmentioning

confidence: 99%

3D Multiple Sound Source Localization by Proposed T-Shaped Circular Distributed Microphone Arrays in Combination with GEVD and Adaptive GCC-PHAT/ML Algorithms

Firoozabadi

Irarrázaval

Adasme

et al. 2022

Sensors

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Multiple simultaneous sound source localization (SSL) is one of the most important applications in the speech signal processing. The one-step algorithms with the advantage of low computational complexity (and low accuracy), and the two-step methods with high accuracy (and high computational complexity) are proposed for multiple SSL. In this article, a combination of one-step-based method based on the generalized eigenvalue decomposition (GEVD), and a two-step-based method based on the adaptive generalized cross-correlation (GCC) by using the phase transform/maximum likelihood (PHAT/ML) filters along with a novel T-shaped circular distributed microphone array (TCDMA) is proposed for 3D multiple simultaneous SSL. In addition, the low computational complexity advantage of the GCC algorithm is considered in combination with the high accuracy of the GEVD method by using the distributed microphone array to eliminate spatial aliasing and thus obtain more appropriate information. The proposed T-shaped circular distributed microphone array-based adaptive GEVD and GCC-PHAT/ML algorithms (TCDMA-AGGPM) is compared with hierarchical grid refinement (HiGRID), temporal extension of multiple response model of sparse Bayesian learning with spherical harmonic (SH) extension (SH-TMSBL), sound field morphological component analysis (SF-MCA), and time-frequency mixture weight Bayesian nonparametric acoustical holography beamforming (TF-MW-BNP-AHB) methods based on the mean absolute estimation error (MAEE) criteria in noisy and reverberant environments on simulated and real data. The superiority of the proposed method is presented by showing the high accuracy and low computational complexity for 3D multiple simultaneous SSL.

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Section: The Evaluation's Scenariosmentioning

confidence: 99%

3D Multiple Sound Source Localization by Proposed T-Shaped Circular Distributed Microphone Arrays in Combination with GEVD and Adaptive GCC-PHAT/ML Algorithms

Firoozabadi

Irarrázaval

Adasme

et al. 2022

Sensors

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show abstract

“…A good example is the Ambisonics system, in which the sound field recordings are made with various Ambisonics microphones ranging from first to fourth order. For this system, different algorithms and methods have been developed to improve the speech intelligibility / music quality / channel separation for a sound source positioned in the chosen direction [15].…”

Section: Listening Tests Setupmentioning

confidence: 99%

Speaker Discrimination in Multisource Environments Auralized in Real Rooms

Jambrošić¹,

Horvat²,

Kisić³

et al. 2020

Akustika

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With the recent development of audio in modern VR/AR systems and the increasing capability of synthesizing natural sound fields over headphones with head tracking, the question of the ability of our hearing system to discriminate multiple concurrent sound sources has become important again. We must understand how psychoacoustical and psychophysical limitations of the hearing system cope with novel technologies of virtual acoustics that can simulate an almost unlimited number of sound sources. Previous research has shown that the capacity of human hearing to discriminate a reference sound source is limited when there is background noise, a reverberant surrounding, or when other, disturbing sound sources simultaneously mask the reference source. A set of listening tests based on the cocktail-party effect was designed to determine the intelligibility of speech emitted by a reference sound source, with one to six disturbing sound sources simultaneously emitting speech from different directions around the listener. The tests were repeated in three test rooms with different acoustical properties, and two test signals were used: logatomes and regular spoken sentences with specific keywords. The results have revealed the changes in speech intelligibility scores in relation to the number of disturbing sources, their positions, and acoustical properties of test rooms.

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Two-dimensional detection based LRSS point recognition for multi-source DOA estimation

Jia

Gao

et al. 2022

Applied Acoustics

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Multiple Sound Sources Localization with Frame-by-Frame Component Removal of Statistically Dominant Source

Cited by 6 publications

References 32 publications

3D Multiple Sound Source Localization by Proposed T-Shaped Circular Distributed Microphone Arrays in Combination with GEVD and Adaptive GCC-PHAT/ML Algorithms

3D Multiple Sound Source Localization by Proposed T-Shaped Circular Distributed Microphone Arrays in Combination with GEVD and Adaptive GCC-PHAT/ML Algorithms

Speaker Discrimination in Multisource Environments Auralized in Real Rooms

Two-dimensional detection based LRSS point recognition for multi-source DOA estimation

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