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Can Kayser1, Adam Kujawski1, Ennes Sarradj1

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The trained iterative soft thresholding algorithm (TISTA) offers a computationally efficient method for microphone array signal processing. This data-driven approach shows improved robustness and accuracy, particularly for localizing numerous sound sources.

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Area of Science:

  • Acoustics
  • Signal Processing
  • Computational Auditory Scene Analysis

Background:

  • Microphone arrays are crucial for sound source characterization and localization.
  • Current signal processing algorithms are computationally intensive, necessitating exploration of alternative methods.
  • Inverse problems in acoustics require efficient and robust solvers.

Purpose of the Study:

  • To conduct an in-depth analysis of the trained iterative soft thresholding algorithm (TISTA) for microphone array processing.
  • To evaluate the robustness and frequency dependence of TISTA.
  • To compare TISTA's performance against existing methods, specifically covariance matrix fitting.

Main Methods:

  • Utilized synthesized and real-world microphone array measurement data.
  • Applied the trained iterative soft thresholding algorithm (TISTA), a data-driven solver for inverse problems.
  • Compared TISTA's performance with a covariance matrix fitting inverse method.

Main Results:

  • TISTA demonstrates favorable performance compared to covariance matrix fitting.
  • The algorithm shows particular effectiveness in scenarios with a large number of sound sources.
  • Analysis provides insights into TISTA's robustness and behavior across different frequencies.

Conclusions:

  • TISTA presents a promising, computationally efficient alternative for microphone array signal processing.
  • The data-driven approach of TISTA offers advantages in accuracy and robustness, especially for complex acoustic environments.
  • Further investigation into TISTA's capabilities can advance sound source localization techniques.