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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Dual Microphone Voice Activity Detection Based on Reliable Spatial Cues.

Soojoong Hwang1, Yu Gwang Jin2, Jong Won Shin3

  • 1School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, 123 Cheomdan-gwagiro, Buk-gu, Gwangju 61005, Korea.

Sensors (Basel, Switzerland)
|August 3, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a frequency-selective approach for dual microphone voice activity detection (VAD). By focusing on reliable frequency bins, it improves VAD performance in noisy and reverberant conditions.

Keywords:
dual microphonefrequency selectioninterchannel level differenceinterchannel time differencevoice activity detection

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Area of Science:

  • Signal Processing
  • Acoustics
  • Machine Learning

Background:

  • Dual microphone systems utilize interchannel time difference (ITD) and interchannel level difference (ILD) for voice activity detection (VAD).
  • Background noise and reverberation can degrade the effectiveness of traditional VAD methods that use all frequency information.
  • Conventional VAD approaches often apply fixed weights across all frequencies, potentially diminishing performance.

Purpose of the Study:

  • To propose a novel dual microphone voice activity detection (VAD) scheme.
  • To leverage spatial cues from reliable frequency bins for enhanced VAD performance.
  • To address the limitations of conventional VAD in noisy and reverberant environments.

Main Methods:

  • A frequency-selective VAD scheme is proposed, utilizing only reliable time-frequency bins.
  • Reliability of each bin is determined by signal energy, ILD, and ITD conditions.
  • ITD- and ILD-based VAD statistics from selected bins are combined for final VAD results.

Main Results:

  • The proposed method selectively uses spatial cues from reliable frequency bins.
  • Experimental results demonstrate enhanced VAD performance in realistic acoustic environments.
  • The frequency-selective approach proves more robust against noise and reverberation compared to conventional methods.

Conclusions:

  • Focusing on reliable frequency bins significantly improves dual microphone VAD.
  • The proposed VAD scheme offers enhanced robustness in challenging acoustic conditions.
  • This approach effectively utilizes complementary spatial information from ITD and ILD.