Non-contact acoustic diagnosis is attractive for automotive maintenance because abnormal sounds can be recorded without installing additional sensors on compact or inaccessible components. Practical service recordings, however, are affected by low-frequency dominance, transient impacts, device differences, propagatio...
Comparisons with Support Vector Machine, convolutional neural network, and standalone Transformer baselines show that MFA-Net provides competitive classification performance and stronger pump diagnosis under several noisy conditions, while computational-cost analysis indicates an inference latency of 6.0401 ms per samp...
In the context of Industry 4.0, sound-based anomaly detection has emerged as a relevant strategy to support predictive maintenance and reduce unplanned downtime in industrial environments. This study investigates the impact of different time–frequency representations on acoustic anomaly detection in industrial machines...
Dog barks serve as key bioacoustic signals for pet monitoring and urban noise early warning. Conventional recognition methods suffer severe feature distortion in noisy environments, while deep learning models carry excessive parameters and slow edge inference. This work proposes an algorithm combining spectral entropy-...
The need for further research in the development of lightweight models for mobile devices, multi-distortion suppression methods, and the integration of neural network noise suppression with generative models to achieve a new level of speech signal restoration quality is demonstrated.
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