Fine-Grained Engine Fault Sound Event Detection Using Multimodal Signals
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(2024)
摘要
Sound event detection (SED) is an active area of audio research that aims to
detect the temporal occurrence of sounds. In this paper, we apply SED to engine
fault detection by introducing a multimodal SED framework that detects
fine-grained engine faults of automobile engines using audio and
accelerometer-recorded vibration. We first introduce the problem of engine
fault SED on a dataset collected from a large variety of vehicles with
expertly-labeled engine fault sound events. Next, we propose a SED model to
temporally detect ten fine-grained engine faults that occur within vehicle
engines and further explore a pretraining strategy using a large-scale
weakly-labeled engine fault dataset. Through multiple evaluations, we show our
proposed framework is able to effectively detect engine fault sound events.
Finally, we investigate the interaction and characteristics of each modality
and show that fusing features from audio and vibration improves overall engine
fault SED capabilities.
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关键词
Sound event detection,Engine fault detection
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