skip to main content
10.1145/3581807.3581861acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiccprConference Proceedingsconference-collections
research-article

DOA Estimation of Multiple Sources based on the Angle Distribution of Time-frequency Points in Single-source Zone

Published:22 May 2023Publication History

ABSTRACT

Direction-of-arrival (DOA) estimation method based on single-source zone (SSZ) detection, using the sparsity of speech signal, which transforms the multiple sources localization into single source localization. However, there are many time-frequency (TF) points whose direction information are far away from the true DOA in the detected SSZ, these points may disturb the localization performance. Aiming this issue, a DOA estimation of multiple sources based on the angle distribution of TF points is proposed in this paper. Firstly, the SSZs are detected through the recorded signal of sound field microphone. Secondly, the optimized single-source zone (OSSZ) can be acquired by removing the outliers based on the angle distribution of the TF points in the detected SSZ. Thirdly, DOA histogram can be obtained using the TF points in OSSZ, then the envelop of the DOA histogram is gained by kernel density estimation. Finally, peak search is adopted to obtain the DOA estimates and number of sources. The experiment results show that the proposed method can achieve better localization performance than SSZ-based method under medium and high reverberation conditions.

References

  1. T. Long, J. Chen, G. Huang, J. Benesty, and I. Cohen. Acoustic Source Localization Based on Geometric Projection in Reverberant and Noisy Environments. IEEE J. Sel. Topics Signal Process. 2019; 13(1):143-155.Google ScholarGoogle ScholarCross RefCross Ref
  2. L. Madmoni and B. Rafaely. Direction of Arrival Estimation for Reverberant Speech Based on Enhanced Decomposition of the Direct Sound. IEEE J. Sel. Topics Signal Process. 2019; 13(1):131-142.Google ScholarGoogle ScholarCross RefCross Ref
  3. A. Lombard, Y. Zheng, H. Buchner, and W. Kellermann. TDOA estimation for multiple sound sources in noisy and reverberant environments using broadband independent component analysis. IEEE Trans. Audio, Speech, Lang. Process., vol. 19, no. 6, pp. 1490–1503, Aug. 2011.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Y. Hu, T. D. Abhayapala, and P. N. Samarasinghe. Multiple Source Direction of Arrival Estimations Using Relative Sound Pressure Based MUSIC. IEEE/ACM Trans. Audio, Speech, Lang. Process., vol. 29, pp. 253–264, 2021.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. G. Huang, J. Chen, and J. Benesty. Direction-of-arrival estimation of passive acoustic sources in reverberant environments based on the householder transformation,” J. Acoust. Soc. Amer., vol. 138, no. 5, pp. 3053–3060, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  6. L. Li, M. Jia, and J. Wang. DOA estimation of multiple speech sources based on the single-source point detection using an FOA microphone. Appl. Acoust., vol. 195, pp. 1–16, 2022.Google ScholarGoogle ScholarCross RefCross Ref
  7. D. Pavlidi, A. Griffin, M. Puigt and A. Mouchtaris. Real-time multiple sound source localization and counting using a circular microphone array. IEEE Trans. Audio Speech Lang. Process., vol. 21, no. 10, pp. 2193–2206, Oct. 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. M. Jia, J. Sun, and C. Bao. Real-Time Multiple Sound Source Localization and Counting Using a Soundfield Microphone. J. Ambient Intell. Humaniz. Comput., vol. 8, no. 6, pp. 829–844, Nov. 2017.Google ScholarGoogle ScholarCross RefCross Ref
  9. M. Jia, J. Sun, C. Bao, and C. Ritz. Separation of multiple speech sources by recovering sparse and non-sparse components from B-format microphone recordings. Speech Commun., vol. 96, pp. 184–196, 2018.Google ScholarGoogle ScholarCross RefCross Ref
  10. M. Jia, Y. Wu, C. Bao, and C. Ritz. Multi-source DOA Estimation in Reverberant Environments by Jointing Detection and Modeling of Time-Frequency Points. IEEE/ACM Trans. Audio, Speech, Lang. Process., vol. 29, pp. 379–392, 2021.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. F. Zotter and M. Frank. Ambisonics: A Practical 3D Audio Theory for Recording, Studio Production, Sound Reinforcement, and Virtual Reality. Springer, 2019.Google ScholarGoogle Scholar
  12. Y. Zou, W. Shi, B. Li, C. Ritz, M. Shujau, and J. Xi. Multi-source DOA estimation based on time-frequency sparsity and joint inter-sensor data ratio with single acoustic vector sensor. In Proceedings of IEEE Int. Conf. Acoust., Speech, Signal Process., 2013, pp. 4011–4015.Google ScholarGoogle Scholar
  13. Z. I. Botev, J. F. Grotowski, and D. P. Kroese. Kernel density estimation via diffusion. The Annals of Statistics., vol. 38, no. 5, pp. 2916–2957, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  14. R. Campbell, K. J. Palomki, and G. J. Brown. A MATLAB simulation of shoebox room acoustics for use in research and teaching. J. Comput. Inf. Syst., vol. 9, no. 3, pp. 48–51, 2005.Google ScholarGoogle Scholar
  15. K. Wu, V. G. Reju, and A. W. H. Khong. Multisource DOA estimation in a reverberant environment using a single acoustic vector sensor. IEEE/ACM Trans. Audio, Speech, Lang. Process., vol. 26, no. 10, pp. 1848–59, 2018.Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. DOA Estimation of Multiple Sources based on the Angle Distribution of Time-frequency Points in Single-source Zone

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Other conferences
        ICCPR '22: Proceedings of the 2022 11th International Conference on Computing and Pattern Recognition
        November 2022
        683 pages
        ISBN:9781450397056
        DOI:10.1145/3581807

        Copyright © 2022 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 22 May 2023

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article
        • Research
        • Refereed limited
      • Article Metrics

        • Downloads (Last 12 months)13
        • Downloads (Last 6 weeks)1

        Other Metrics

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format .

      View HTML Format