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Fast Sound Source Localization Using Two-Level Search Space Clustering.

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    We developed a faster method for sound source localization (SSL) using steered response power phase transform (SRP-PHAT). Our technique significantly reduces computation time for large microphone arrays without sacrificing accuracy.

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    Area of Science:

    • Acoustics
    • Signal Processing
    • Machine Learning

    Background:

    • Sound source localization (SSL) is crucial for many applications.
    • Steered Response Power Phase Transform (SRP-PHAT) is a common SSL method.
    • SRP-PHAT's computational cost limits its real-time application in large microphone arrays.

    Purpose of the Study:

    • To accelerate the SRP-PHAT algorithm for real-time SSL.
    • To reduce computational complexity in large-scale microphone array systems.
    • To maintain localization accuracy while improving speed.

    Main Methods:

    • Proposed a novel two-level search space clustering method.
    • Divided candidate sound source locations into groups.
    • Identified promising groups likely containing the maximum power location for focused search.

    Main Results:

    • Achieved a 61.8% reduction in computational costs compared to prior methods.
    • Maintained the accuracy of sound source localization.
    • Demonstrated the efficiency of the two-level clustering approach.

    Conclusions:

    • The proposed method significantly speeds up SRP-PHAT-based SSL.
    • This approach enables real-time sound source localization in large microphone arrays.
    • The method offers a practical solution for computationally intensive SSL tasks.