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A model-based Bayesian framework for sound source enumeration and direction of arrival estimation using a coprime

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Summary
This summary is machine-generated.

This study uses Bayesian inference and a novel model to determine the number and location of sound sources. The method effectively handles multiple concurrent sound sources, improving acoustic source identification.

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Area of Science:

  • Acoustics
  • Signal Processing
  • Statistical Inference

Background:

  • Coprime microphone arrays offer enhanced degrees of freedom and resolve grating lobe ambiguities.
  • Narrow beams are achieved above the spatial Nyquist limit, but aliasing causes residual side lobes.
  • Previous work mitigates side lobes for broadband sources, but accurately identifying source count remains a challenge.

Purpose of the Study:

  • To develop a model for scenes with multiple concurrent sound sources.
  • To employ Bayesian inference for model selection and parameter estimation.
  • To accurately determine the number and location of acoustic sources.

Main Methods:

  • A linear combination of Laplace distribution functions models multiple sound sources.
  • Bayesian inference is used to select the most probable model from competing hypotheses.
  • Nested sampling, a Markov Chain Monte Carlo method, explores the likelihood function and evaluates Bayesian evidence.

Main Results:

  • The proposed Bayesian approach successfully distinguishes between competing models of sound source configurations.
  • Model parameters, including source locations, are estimated with improved accuracy.
  • The method effectively penalizes model complexity using an Occam's razor principle.

Conclusions:

  • This work provides a robust framework for identifying multiple concurrent sound sources using coprime microphone arrays.
  • The Bayesian inference method offers a principled way to address model uncertainty in acoustic source localization.
  • The findings advance the capabilities of acoustic sensing and source separation techniques.