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Multiple Sound Sources Localization by using Statistical Source Component Equalization

Published: 25 March 2020 Publication History

Abstract

Multiple sound sources localization is a hot topic in audio signal processing as it can provide effective information for parameter coding and reconstruction of sound scenes. In this paper, a multiple sound sources localization method is proposed by using statistical source component equalization. Based on single source zone (SSZ) detection, the proposed method aims to settle the localization accuracy degradation problem caused by the missed detection of statistically weak source (SWS) which is inevitable in the sound scene where more than five sound sources occur simultaneously. Since SWSs only have little DOA estimations compared with other sound sources called statistically dominant source (SDS), they are difficult to be found in the histogram of DOA estimations. A statistical source component equalization algorithm is designed to remove the components of SDSs and reserve the components of SWSs at the same time, which can make the SWSs obvious enough to be found through post-processing. The objective evaluation reveals that the proposed method can always obtain a comparable or better localization results than traditional SSZ-based method

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  • (2023)Multi-Source Localization Using Optimized Time-Frequency Representation and Sparsity Component AnalysisIEEE/ACM Transactions on Audio, Speech, and Language Processing10.1109/TASLP.2023.331645031(3564-3578)Online publication date: 2023
  • (2020)Multiple Sound Source Separation by Jointing Single Source Zone Detection and Linearly Constrained Minimum VarianceProceedings of the 2020 9th International Conference on Computing and Pattern Recognition10.1145/3436369.3437435(141-145)Online publication date: 30-Oct-2020

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  1. Multiple Sound Sources Localization by using Statistical Source Component Equalization

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      cover image ACM Other conferences
      ICCPR '19: Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition
      October 2019
      522 pages
      ISBN:9781450376570
      DOI:10.1145/3373509
      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 ACM 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]

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      • Hebei University of Technology
      • Beijing University of Technology

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      New York, NY, United States

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      Published: 25 March 2020

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      Author Tags

      1. Direction of arrival estimation
      2. Multiple sources localization
      3. Sparsity

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      • (2023)Multi-Source Localization Using Optimized Time-Frequency Representation and Sparsity Component AnalysisIEEE/ACM Transactions on Audio, Speech, and Language Processing10.1109/TASLP.2023.331645031(3564-3578)Online publication date: 2023
      • (2020)Multiple Sound Source Separation by Jointing Single Source Zone Detection and Linearly Constrained Minimum VarianceProceedings of the 2020 9th International Conference on Computing and Pattern Recognition10.1145/3436369.3437435(141-145)Online publication date: 30-Oct-2020

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