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

Journal Article


Abstract


  • This paper aims to tackle the multiple source localization problem using the signals recorded by a B-format microphone in reverberant environments. The proposed approach analyses the energy and phase information of recorded signals in the time���frequency (T-F) domain to present a two-dimensional detection of low-reverberant-single-source (LRSS) points, where the pressure���velocity energy difference and pressure���velocity phase difference are exploited. The direction of arrival (DOA) estimation of actual sources by utilizing the designed weight kernel density estimation are conducted base on the direction cues derived from the LRSS points. Evaluation on simulated data and data recorded in an actual acoustic chamber demonstrates that a better performance can be obtained through the proposed algorithm in highly reverberant environments.

Publication Date


  • 2022

Citation


  • Jia, M., Gao, S., Wu, Y., Bao, C., & Ritz, C. (2022). Two-dimensional detection based LRSS point recognition for multi-source DOA estimation. Applied Acoustics, 186. doi:10.1016/j.apacoust.2021.108481

Scopus Eid


  • 2-s2.0-85118560259

Volume


  • 186

Issue


Place Of Publication


Abstract


  • This paper aims to tackle the multiple source localization problem using the signals recorded by a B-format microphone in reverberant environments. The proposed approach analyses the energy and phase information of recorded signals in the time���frequency (T-F) domain to present a two-dimensional detection of low-reverberant-single-source (LRSS) points, where the pressure���velocity energy difference and pressure���velocity phase difference are exploited. The direction of arrival (DOA) estimation of actual sources by utilizing the designed weight kernel density estimation are conducted base on the direction cues derived from the LRSS points. Evaluation on simulated data and data recorded in an actual acoustic chamber demonstrates that a better performance can be obtained through the proposed algorithm in highly reverberant environments.

Publication Date


  • 2022

Citation


  • Jia, M., Gao, S., Wu, Y., Bao, C., & Ritz, C. (2022). Two-dimensional detection based LRSS point recognition for multi-source DOA estimation. Applied Acoustics, 186. doi:10.1016/j.apacoust.2021.108481

Scopus Eid


  • 2-s2.0-85118560259

Volume


  • 186

Issue


Place Of Publication