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Acoustic Event Detection in Vehicles: A Multi-Label Classification Approach.

Anaswara Antony1,2, Wolfgang Theimer2, Giovanni Grossetti2

  • 1Department of Computer Science, University of Applied Sciences and Arts (FH Dortmund), 44227 Dortmund, Germany.

Sensors (Basel, Switzerland)
|April 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an Acoustic Event Detection model for autonomous vehicles, enhancing safety by adding "ears" to complement visual sensors. The model accurately identifies various sounds in real-world driving scenarios.

Keywords:
acoustic event detectionaudio sceneautonomous drivingtransformers

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Area of Science:

  • Artificial Intelligence
  • Robotics
  • Acoustics

Background:

  • Autonomous driving systems primarily rely on visual sensors like cameras, RADAR, and LiDAR for environmental perception.
  • Integrating auditory information can significantly enhance the reliability and safety of driverless vehicles.
  • Current systems lack comprehensive audio-based environmental understanding.

Purpose of the Study:

  • To develop and evaluate an Acoustic Event Detection (AED) model for autonomous driving contexts.
  • To create an audio scene description by detecting acoustic events and their timing.
  • To improve the safety and robustness of autonomous vehicles through enhanced sensory input.

Main Methods:

  • Utilized the pre-trained Bidirectional Encoder representation from Audio Transformers (BEATs) network.
  • Developed a single-layer neural network trained on a diverse database of real automotive audio recordings.
  • Evaluated model performance using various parameters and datasets, including sound mixtures.

Main Results:

  • The model achieved a mean accuracy of 0.93 and an F1-Score of 0.39 for 11 sound classes with a 0.5 confidence threshold.
  • The threshold-independent metric mean Average Precision (mAP) reached 0.77.
  • Performance remained strong for sound mixtures, with mean accuracy, F1-Score, and mAP of 0.89, 0.42, and 0.658, respectively.

Conclusions:

  • The proposed AED model effectively detects acoustic events in automotive environments, contributing to a more comprehensive scene understanding.
  • The integration of audio perception alongside visual data offers a promising pathway to more reliable and safer autonomous driving.
  • The model demonstrates robust performance even with overlapping sound events, highlighting its practical applicability.