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A frequency-domain beamforming for rotating sound source identification.

Wei Ma1, Ce Zhang1

  • 1School of Aeronautics and Astronautics, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, People's Republic of China.

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Summary

This study introduces mode composition beamforming (MCB), a new method for identifying rotating sound sources. MCB overcomes limitations of previous techniques, offering faster computation without requiring a microphone ring.

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Area of Science:

  • Acoustics
  • Signal Processing
  • Mechanical Engineering

Background:

  • Identifying rotating sound sources is crucial for industrial applications.
  • Current frequency-domain beamforming methods rely on virtual rotating arrays (VRA), necessitating a circular microphone arrangement around the source.
  • This constraint limits the practical application of existing techniques in industrial settings.

Purpose of the Study:

  • To propose a novel frequency-domain rotating beamforming method, termed mode composition beamforming (MCB).
  • To overcome the microphone arrangement constraint imposed by virtual rotating array (VRA) methods.
  • To achieve efficient and accurate identification of rotating sound sources.

Main Methods:

  • MCB directly employs delay-and-sum principles combined with the mode composition of rotating sound.
  • The method avoids the use of the virtual rotating array (VRA) concept.
  • Special arrays, like rotational symmetry arrays, are recommended to mitigate Doppler-induced ghost contributions.

Main Results:

  • MCB demonstrates comparable sound source identification capabilities to the time-domain rotating source identifier (ROSI).
  • MCB offers significantly higher computational speed compared to ROSI, particularly with numerous samplers.
  • The method effectively identifies rotating sound sources without the VRA constraint.

Conclusions:

  • Mode composition beamforming (MCB) provides a flexible and efficient alternative for rotating sound source identification in industrial applications.
  • MCB's computational efficiency makes it suitable for scenarios with a large number of data samplers.
  • Further research using specific array geometries can enhance MCB's performance by addressing Doppler effects.