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Multi-Source DOA Estimation in Reverberant Environments by Jointing Detection and Modeling of Time-Frequency Points

Journal Article


Abstract


  • In this article, the direction of arrival (DOA) estimation of multiple speech sources in reverberant environments is investigated based on the recording of a soundfield microphone. First, the recordings are analyzed in the time-frequency (T-F) domain to detect both 'points' (single T-F points) and 'regions' (multiple, adjacent T-F points) corresponding to a single source with low reverberation (known as low-reverberant-single-source (LRSS) points). Then, a LRSS point detection algorithm is proposed based on a joint dominance measure and instantaneous single-source point (SSP) identification. Following this, initial DOA estimates obtained for the detected LRSS points are analyzed using a Gaussian Mixture Model (GMM) derived by the Expectation-Maximization (EM) algorithm to cluster components into sources or outliers using a rule-based method. Finally, the DOA of each actual source is obtained from the estimated source components. Experiments on both simulated data and data recorded in an actual acoustic chamber demonstrate that the proposed algorithm exhibits improved performance for the DOA estimation in reverberant environments when compared to several existing approaches.

Publication Date


  • 2021

Citation


  • Jia, M., Wu, Y., Bao, C., & Ritz, C. (2021). Multi-Source DOA Estimation in Reverberant Environments by Jointing Detection and Modeling of Time-Frequency Points. IEEE/ACM Transactions on Audio Speech and Language Processing, 29, 379-392. doi:10.1109/TASLP.2020.3042705

Scopus Eid


  • 2-s2.0-85097958874

Start Page


  • 379

End Page


  • 392

Volume


  • 29

Issue


Place Of Publication


Abstract


  • In this article, the direction of arrival (DOA) estimation of multiple speech sources in reverberant environments is investigated based on the recording of a soundfield microphone. First, the recordings are analyzed in the time-frequency (T-F) domain to detect both 'points' (single T-F points) and 'regions' (multiple, adjacent T-F points) corresponding to a single source with low reverberation (known as low-reverberant-single-source (LRSS) points). Then, a LRSS point detection algorithm is proposed based on a joint dominance measure and instantaneous single-source point (SSP) identification. Following this, initial DOA estimates obtained for the detected LRSS points are analyzed using a Gaussian Mixture Model (GMM) derived by the Expectation-Maximization (EM) algorithm to cluster components into sources or outliers using a rule-based method. Finally, the DOA of each actual source is obtained from the estimated source components. Experiments on both simulated data and data recorded in an actual acoustic chamber demonstrate that the proposed algorithm exhibits improved performance for the DOA estimation in reverberant environments when compared to several existing approaches.

Publication Date


  • 2021

Citation


  • Jia, M., Wu, Y., Bao, C., & Ritz, C. (2021). Multi-Source DOA Estimation in Reverberant Environments by Jointing Detection and Modeling of Time-Frequency Points. IEEE/ACM Transactions on Audio Speech and Language Processing, 29, 379-392. doi:10.1109/TASLP.2020.3042705

Scopus Eid


  • 2-s2.0-85097958874

Start Page


  • 379

End Page


  • 392

Volume


  • 29

Issue


Place Of Publication