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Separation of Simultaneous Speakers with Acoustic Vector Sensor.

Józef Kotus1, Grzegorz Szwoch1

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This study introduces a novel sound source separation technique using sound intensity analysis for live audio. The method effectively isolates desired sounds, significantly improving speech intelligibility in noisy environments.

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Area of Science:

  • Acoustics
  • Signal Processing
  • Machine Learning

Background:

  • Live audio signals often contain multiple sound sources, making analysis and extraction of specific sounds challenging.
  • Traditional sound source separation methods struggle with real-time processing and complex acoustic environments.
  • Acoustic vector sensors offer rich spatial information crucial for advanced signal processing.

Purpose of the Study:

  • To develop and evaluate a novel sound source separation method for live audio signals.
  • To enhance the performance of automated speech recognition and speaker diarization systems.
  • To enable effective sound separation in scenarios with concurrent speech using compact acoustic sensors.

Main Methods:

  • Sound pressure signals from an acoustic vector sensor are analyzed to compute the 2D spectral distribution of sound intensity.
  • Spectral components are selected based on calculated source direction for spatial filtration.
  • The method was tested with real sensor impulse responses and varying source positions, including multi-source scenarios.

Main Results:

  • The proposed algorithm achieved signal-to-distortion ratio (SDR) values of 10-12 dB.
  • Short-Time Objective Intelligibility Measure (STOI) values improved by 0.15-0.30, reaching 0.86-0.94.
  • Effective sound source separation was demonstrated, even with multiple active sources and varying signal-to-noise ratios.

Conclusions:

  • The sound intensity-based spatial filtration method offers a robust approach to live sound source separation.
  • The technique significantly enhances speech intelligibility and is suitable for real-time applications.
  • This method provides a valuable tool for improving automated speech recognition and speaker diarization systems.