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Related Experiment Video

Updated: Jul 11, 2025

Flying Insect Detection and Classification with Inexpensive Sensors
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Acoustic-Sensing-Based Attribute-Driven Imbalanced Compensation for Anomalous Sound Detection without Machine

Yifan Zhou1, Yanhua Long1,2, Haoran Wei3

  • 1Key Innovation Group of Digital Humanities Resource and Research, Shanghai Normal University, Shanghai 200234, China.

Sensors (Basel, Switzerland)
|November 14, 2023
PubMed
Summary

This study introduces a new method for anomalous sound detection (ASD) using weak attribute labels from acoustic sensors when strong machine identity priors are unavailable. The approach enhances robustness and improves performance in condition monitoring systems.

Keywords:
acoustic sensinganomalous sound detectionattribute classificationcondition monitoringimbalanced compensation

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Area of Science:

  • Acoustic Sensing
  • Machine Learning
  • Condition Monitoring

Background:

  • Anomalous Sound Detection (ASD) is vital for condition monitoring.
  • Unsupervised training data (normal sounds only) presents challenges for robust ASD systems.
  • Existing discriminative models rely on strong prior knowledge (e.g., machine ID), limiting real-world applicability.

Purpose of the Study:

  • To develop a robust ASD system using weak attribute labels from acoustic sensors.
  • To overcome limitations of models requiring strong prior knowledge.
  • To enhance ASD performance in unsupervised and data-scarce scenarios.

Main Methods:

  • Utilizing imbalanced and inconsistent acoustic sensor attributes (e.g., speed, microphone model) as weak priors.
  • Training an attribute classifier with an imbalanced compensation strategy for model trainability.
  • Employing a score fusion method to improve anomaly detection robustness.

Main Results:

  • The proposed algorithm achieved a sixth-place ranking in the DCASE2023 Challenge Task 2.
  • Demonstrated effective ASD performance by exploiting weak attribute knowledge.
  • Showcased the framework's capability in scenarios lacking strong priors.

Conclusions:

  • Exploiting acoustic sensor data attributes as weak priors offers an effective ASD framework.
  • The approach provides a viable solution for robust condition monitoring when strong priors are absent.
  • The developed method enhances ASD system performance in practical, real-world applications.