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A Computationally efficient IVA-based Blind Source Separation for Hearing Aid Applications and its Real-time

Gautam Shredhar Bhat, Chanan Karadagur Ananda Reddy, Nikhil Shankar

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    This study enhances Independent Vector Analysis (IVA) for hearing aid applications using neural network-based sound source localization. The improved method efficiently separates speech and noise in real-time, proving practical for hearing assistance.

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    Area of Science:

    • Signal Processing
    • Artificial Intelligence
    • Acoustics

    Background:

    • Conventional Blind Source Separation (BSS) methods are computationally intensive, limiting their real-time application.
    • Frequent updates of the demixing matrix in BSS contribute to high computational complexity.
    • Existing BSS techniques struggle with efficiency in practical, real-time scenarios.

    Purpose of the Study:

    • To enhance the efficiency of Independent Vector Analysis (IVA), a Blind Source Separation (BSS) method.
    • To develop a computationally efficient real-time sound source separation technique for hearing aid applications.
    • To integrate neural network-based sound source localization with IVA for improved performance.

    Main Methods:

    • A neural network-based two-microphone sound source localization method was employed as a criterion.
    • Independent Vector Analysis (IVA) was utilized for separating convolutedly mixed speech and noise sources.
    • The proposed method was implemented on a smartphone for real-time testing in realistic acoustic environments.

    Main Results:

    • The integration of sound source localization with IVA significantly enhanced BSS efficiency.
    • The method demonstrated practical usability by successfully separating speech and noise in real-time on a smartphone.
    • Objective and subjective tests confirmed the effectiveness of the developed approach.

    Conclusions:

    • The proposed neural network-enhanced IVA method offers a practical solution for real-time speech and noise separation.
    • This technique is highly beneficial for improving the performance of hearing aid devices.
    • The study validates the method's usefulness for real-world hearing assistance applications.