Multiple Sound Source Localization and Counting Using One Pair of Microphones in Noisy and Reverberant Environments

Fang, Yuzhuo; Xu, Zhihai

doi:10.1155/2020/8937829

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…In some applications, these systems can even outperform the human hearing apparatus. Reverberant and noisy environments are especially demanding and challenging, since the localization performance suffers due to strong reflections and background noise sources added to the direct sound of the observed sound source [13]. To improve performance, mobile robots often use more than two microphones configured as an array that can be optimized for different applications, sound source types or environments [14].…”

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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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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show abstract

“…Jiang et al [18] proposed a new algorithm combining deep fusion and Convolutional Neural Network in response to the problem of inaccurate sound source localisation in a reverberant environment. Combining double-wide matching pursuit method, Fang et al [19] proposed a multi-sound source localisation counting technique. The sound source location accuracy and absolute error analysis results show that this methodology has better accuracy in the conditions of strong reverberation and multiple sound sources.…”

Section: Introductionmentioning

confidence: 99%

An improved deconvolution beamforming algorithm for acoustic imaging of low signal-to-noise ratio sound sources in reverberant field

Guo¹,

Chen²,

Xia³

et al. 2023

J. vibroeng.

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Most of the existing acoustic imaging studies in reverberant field ignore the influence of signal-to-noise ratio. As a result, commonly used beamforming algorithms in reverberant backgrounds have poor imaging accuracy for low signal-to-noise ratio sound sources. In response to that problem, an improved adaptive beamforming algorithm called SC-DAMAS is put forward in this paper. The algorithm replaces the free-field Green's function with the impulse response function, making the algorithm more suitable for acoustic imaging of low signal-to-noise ratio in a reverberant environment. Besides, the comparative simulation results with the conventional beamforming method and orthogonal matching pursuit algorithm-based DAMAS, as well as sound source acoustic imaging experiments are carried out to analyze its effectiveness. It is indicated that, in the reverberation field, the SC-DAMAS has no obvious sidelobes and achieves higher positioning accuracy for acoustic imaging of low signal-to-noise ratio sound source than the abovementioned counterparts, and its imaging test result is consistent with the actual situation, which verifies the effectiveness of the algorithm.

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“…erefore, the second step requires the active sources to be located and counted simultaneously [8] from the constructed spectrum. Current source localization and counting methods mainly use iterative search methods, such as single-point peak amplitude in peak search (PS) [24,25], inner product in matching pursuit (MP) [26,27], and source contribution rate in iterative contribution removal (ICR) [15]. Each iteration selects the optimal value satisfying the corresponding conditions, removes the components corresponding to the current sound source from the spectrum, and restarts the next.…”

Section: Introductionmentioning

confidence: 99%

A Multiple Sound Source Localization and Counting Method Based on the Kernel Density Estimator

Fang

Zang

Yang

et al. 2022

Mathematical Problems in Engineering

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An unambiguous signal processing algorithm when using a wide intermicrophone distance is proposed in this paper for simultaneously locating and counting multiple active sound sources. Based on the kernel density estimator, a multistage structure in the time-frequency domain is used to suppress the influence of spatial aliasing, then the pooled angular spectrum is combined with a peak search method having an updated cut-off threshold and a source merging module. Complete source localization and counting is realized through the combination of these two steps. Simulation results show that the proposed method has a more robust performance than the classic counterpart, especially in adverse environments with spatial aliasing, reverberation, and interference between different sound sources.

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Multiple Sound Source Localization and Counting Using One Pair of Microphones in Noisy and Reverberant Environments

Cited by 3 publications

References 25 publications

Speaker Discrimination in Multisource Environments Auralized in Real Rooms

Speaker Discrimination in Multisource Environments Auralized in Real Rooms

An improved deconvolution beamforming algorithm for acoustic imaging of low signal-to-noise ratio sound sources in reverberant field

A Multiple Sound Source Localization and Counting Method Based on the Kernel Density Estimator

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