Multiple-to-single sound source localization by applying single-source bins detection

Jia, Maoshen; Sun, Jundai; Bao, Changchun; Ritz, Christian

doi:10.1016/j.apacoust.2018.03.015

Cited by 12 publications

(2 citation statements)

References 16 publications

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“…where cardð•Þ denotes the calculation of the number of elements in a set. According to (25) and (37), the index sets of ZFLs in the SSZs detected by the two methods can be acquired as…”

Section: Ssz Detection Resultsmentioning

confidence: 99%

“…The approximate WDO reveals the sparsity of speech in the recorded signal of multiple sources scenario, that is, the time-frequency (TF) components of different source are disjoint in the TF domain [21,22], that is, the spectra of different speech source are hardly non-overlapped [23,24]. Following the WDO, we know that there are many TF points where only one source is dominant, and these points are called single-source points (SSPs) [25]. In an SSP, the dominant source is much more energetic than other sources or interference components, and its DOA information is close to that of the real source.…”

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confidence: 99%

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Multisource localization based on angle distribution of time–frequency points using an FOA microphone

Tao

Jia

et al. 2023

CAAI Trans on Intel Tech

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Multisource localization occupies an important position in the field of acoustic signal processing and is widely applied in scenarios, such as human‐machine interaction and spatial acoustic parameter acquisition. The direction‐of‐arrival (DOA) of a sound source is convenient to render spatial sound in the audio metaverse. A multisource localization method in a reverberation environment is proposed based on the angle distribution of time–frequency (TF) points using a first‐order ambisonics (FOA) microphone. The method is implemented in three steps. 1) By exploring the angle distribution of TF points, a single‐source zone (SSZ) detection method is proposed by using a standard deviation‐based measure, which reveals the degree of convergence of TF point angles in a zone. 2) To reduce the effect of outliers on localization, an outlier removal method is designed to remove the TF points whose angles are far from the real DOAs, where the median angle of each detected zone is adopted to construct the outlier set. 3) DOA estimates of multiple sources are obtained by postprocessing of the angle histogram. Experimental results in both the simulated and real scenarios verify the effectiveness of the proposed method in a reverberation environment, which also show that the proposed method outperforms reference methods.

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“…where cardð•Þ denotes the calculation of the number of elements in a set. According to (25) and (37), the index sets of ZFLs in the SSZs detected by the two methods can be acquired as…”

Section: Ssz Detection Resultsmentioning

confidence: 99%

mentioning

confidence: 99%