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Area of Science:

  • Acoustics
  • Signal Processing
  • Applied Mathematics

Background:

  • Compressive sensing (CS) has gained prominence in acoustics over the past decade.
  • CS originates from signal processing and applied mathematics, demonstrating significant acoustic applications.

Purpose of the Study:

  • This special issue focuses on compressive sensing applications in acoustics.
  • Key areas explored include compressive beamforming and holography.

Main Methods:

  • CS reconstructs the direction of arrival (DOA) of multiple acoustic sources using sparsity constraints on sensor array data.
  • Improved sound field reconstruction in holography is achieved via sparsity constraints compared to conventional ℓ2-regularization.

Main Results:

  • CS enables accurate DOA estimation of multiple sound sources.
  • Sparsity-based methods enhance acoustic holography reconstruction fidelity.

Conclusions:

  • Compressive sensing offers powerful tools for acoustic analysis, including beamforming and holography.
  • Future research directions include sparse array configurations and sparse sensing for acoustic communication.