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Sound Source Localization Algorithm of Microphone Array Based on Incremental Broad Learning System

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Abstract

Sound source localization is a technique that utilizes microphone arrays to detect the position of sound sources. It has a wide range of applications in areas such as smart homes, robot navigation, and conference recording. However, due to the complexity of the acoustic environment and the impact of noise interference, the accuracy of localization algorithms has always been a core concern in this field. Traditional time difference of arrival (TDOA) techniques struggle to achieve high precision and efficiency in localization results. To address this issue, this paper proposes a microphone array sound source localization method based on incremental broad learning (Enhance) algorithm. This method extracts shallow and deep features from audio signals and maps them to feature nodes and enhancement nodes in the broad learning system (BLS); a neural network model is constructed, which allows for fast adjustment of network structure and parameters. The model employs ridge regression to calculate connection weights and utilizes enhancement nodes to modify and optimize the feature nodes, thus achieving accurate prediction of sound source locations. The proposed method is experimentally validated using the NOIZEUS dataset and compared with the single-structure broad learning (One-shot) algorithm, back-propagation neural network (BP) algorithm, and recurrent neural network (RNN) algorithm. In the experiments, a microphone array consisting of four microphones is used, with a room size of 5 m × 4 m × 3 m. Different reverberation times (T60) and signal-to-noise ratios (SNRs) are employed to simulate various acoustic environments, and the performance of the four algorithms is evaluated in terms of outlier percentage and mean-squared error (MSE). The results demonstrate that under high reverberation (T60 = 700 ms) and low SNR (SNR = 0 dB) conditions, the proposed method achieves outlier percentage of only 0.308% and MSE of 0.92°. Compared to the other three algorithms, Enhance algorithm exhibits superior localization accuracy, noise robustness, and stability, thus holding significant research and practical value.

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Data Availability

The datasets and codes of this paper for the simulation are available from the corresponding author if requested.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their constructive comments and recommendations.

Funding

This research was funded by the National Natural Science Foundation of China (Grant No. 52165010), National Natural Science Foundation of China (Grant No. 52065013), Liuzhou Science and Technology Project: Development of key technologies for improving the fuel economy of commercial vehicles (2021AAA0108).

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Authors and Affiliations

  1. School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin, 541004, China

    Rongjiang Tang, Yue Zhang, Yingxiang Zuo, Bo Lin & Meng Liang

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  1. Rongjiang Tang
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  2. Yue Zhang
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  3. Yingxiang Zuo
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  4. Bo Lin
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  5. Meng Liang
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Contributions

Rongjiang Tang did conceptualization and funding acquisition and writing—review and editing; Yue Zhang contributed to methodology, writing—original draft preparation; Yingxiang Zuo contributed to software; Bo Lin and Meng Liang performed formal analysis. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Rongjiang Tang.

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Tang, R., Zhang, Y., Zuo, Y. et al. Sound Source Localization Algorithm of Microphone Array Based on Incremental Broad Learning System. Circuits Syst Signal Process 43, 1549–1571 (2024). https://doi.org/10.1007/s00034-023-02521-0

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  • DOI: https://doi.org/10.1007/s00034-023-02521-0

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