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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Angle-dependent phononic dynamics for data-driven source localization.

Weidi Wang1, Amir Ashkan Mokhtari2, Ankit Srivastava2

  • 1Department of Mechanical Engineering, University of Massachusetts, Lowell, Lowell, Massachusetts 01854, USA.

The Journal of the Acoustical Society of America
|November 8, 2023
PubMed
Summary
This summary is machine-generated.

This study uses phononic arrays and exceptional points to precisely locate sound sources. An artificial neural network accurately identifies incident angles, outperforming traditional methods.

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Area of Science:

  • Acoustics
  • Materials Science
  • Artificial Intelligence

Background:

  • The source angle localization problem is crucial in various sensing applications.
  • Exceptional points in phononic crystals offer unique modal behaviors for enhanced sensitivity.
  • Existing localization methods can be improved with advanced signal processing techniques.

Purpose of the Study:

  • To investigate the use of phononic arrays and exceptional points for 2D elastic wave scattering analysis.
  • To develop a data-driven approach for accurate source angle localization.
  • To enhance the performance of existing localization techniques.

Main Methods:

  • Analysis of equi-frequency band structures and angle-dependent modal behaviors in phononic arrays.
  • Exploiting exceptional points and critical angles for enhanced sensitivity.
  • Training an artificial neural network with eigen-modes for deep learning of scattering dynamics.

Main Results:

  • The trained neural network accurately classifies incident angles of scattering signals.
  • The proposed method demonstrates superior localization performance compared to standard delay-and-sum techniques.
  • Minimal sidelobe levels and suppressed main lobewidth were achieved.

Conclusions:

  • Phononic arrays and exceptional points provide a physically relevant platform for advanced sensing.
  • The data-driven approach using artificial neural networks significantly improves source angle localization.
  • This method offers a robust enhancement to existing localization techniques, leveraging band topology and eigen-modes.