Robust automatic train pass-by detection combining deep learning and sound level analysis

Erwann Betton-Ployon ^1,2, Abbes Kacem ¹, Jérôme Mars ²

⁰ACOUSTB, Saint-Martin-d'Héres, 38400, France.

Jasa Express Letters +

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May 19, 2025

View abstract on PubMed

Summary

This study introduces an automatic method for detecting train pass-bys, crucial for managing noise pollution. The approach achieves high accuracy, enabling better assessment of railway noise in various environments.

Area Of Science

Acoustics and Signal Processing
Environmental Noise Monitoring

Background

Growing demand for controlling high noise levels necessitates advanced automatic sound event detection.
Limited research exists on automatic train pass-by detection, despite its significant annoyance factor.

Purpose Of The Study

To develop an innovative and accurate method for automatic train pass-by detection.
To improve the estimation of railway noise contribution in diverse soundscapes.

Main Methods

A generic classifier identifies vehicle noise from raw audio signals.
Mel-spectrogram analysis and sound level metrics refine detection to isolate train pass-bys.
The method was tested on various long-term audio signals.

Main Results

Achieved a 90% temporal overlap with reference demarcations for train pass-by events.
Demonstrated high detection rates on diverse, long-term audio recordings.
The developed technique effectively distinguishes train noise from other vehicle sounds.

Conclusions

The proposed method offers a reliable solution for automatic train pass-by detection.
High detection accuracy facilitates precise railway noise contribution assessment.
This advancement supports better management of noise pollution in urban and rural soundscapes.