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Anomalous sound event detection: A survey of machine learning based methods and applications

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Abstract

With the development of multi-modal man-machine interaction, audio signal analysis is gaining importance in a field traditionally dominated by video. In particular, anomalous sound event detection offers novel options to improve audio-based man-machine interaction, in many useful applications such as surveillance systems, industrial fault detection and especially safety monitoring, either indoor or outdoor. Event detection from audio can fruitfully integrate visual information and can outperform it in some respects, thus representing a complementary perceptual modality. However, it also presents specific issues and challenges. In this paper, a comprehensive survey of anomalous sound event detection is presented, covering various aspects of the topic, ı.e.feature extraction methods, datasets, evaluation metrics, methods, applications, and some open challenges and improvement ideas that have been recently raised in the literature.

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Acknowledgements

This work has been funded by the University of Genoa in the framework of the project Xpert.

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  1. Electrical Eng. Dept., ENIT, University of Tunis El Manar, Tunis El Manar, Tunisia

    Zied Mnasri

  2. University of Genoa, Genoa, Italy

    Zied Mnasri, Stefano Rovetta & Francesco Masulli

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Mnasri, Z., Rovetta, S. & Masulli, F. Anomalous sound event detection: A survey of machine learning based methods and applications. Multimed Tools Appl 81, 5537–5586 (2022). https://doi.org/10.1007/s11042-021-11817-9

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