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Robust and early howling detection based on a sparsity measure.

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This study introduces NINOS-Transposed (NINOS-T) for improved howling detection in audio systems. The new method enhances acoustic feedback suppression, offering greater robustness and earlier artifact identification.

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Area of Science:

  • Acoustics and Signal Processing
  • Audio Engineering
  • Machine Learning for Audio

Background:

  • Acoustic feedback, or howling, is a persistent issue in sound systems like public address and hearing aids.
  • This feedback arises from the microphone-loudspeaker coupling, creating instability and audible howling artifacts.
  • Notch-filter-based howling suppression (NHS) aims to stabilize systems by detecting and removing howling.

Purpose of the Study:

  • To introduce a novel howling detection (HD) feature, NINOS-Transposed (NINOS-T), for more effective acoustic feedback suppression.
  • To present a new, larger, and more diverse annotated dataset for HD research.
  • To propose an improved HD performance evaluation procedure suitable for features without candidate selection.

Main Methods:

  • Development of the NINOS-Transposed (NINOS-T) feature, leveraging the time-frequency structure of howling artifacts.
  • Creation of a comprehensive annotated dataset featuring realistic howling.
  • Introduction of a new evaluation procedure using precision-recall curves to address class imbalance and enable early detection.

Main Results:

  • The NINOS-T feature demonstrates superior performance compared to existing state-of-the-art HD features.
  • NINOS-T exhibits increased robustness to variations in detection thresholds.
  • The proposed evaluation procedure effectively handles class imbalance and assesses early howling and ringing detection.

Conclusions:

  • The NINOS-T feature offers a significant advancement in howling detection, outperforming current methods.
  • The new dataset and evaluation procedure facilitate more rigorous research and development in acoustic feedback suppression.
  • This work contributes to more stable and clearer audio experiences in various sound reinforcement applications.