Abstract
Sound plays an important role in human daily life as humans use sound to communicate with each other and to understand the events occurring in the surroundings. This has prompted the researchers to further study on how to automatically identify the event that is happening by analyzing the acoustic signal. This paper presents a deep learning model enhanced by compressed sensing techniques for acoustic event classification. The compressed sensing first transforms the input acoustic signal into a reconstructed signal to reduce the noise in the input acoustic signal. The reconstructed signals are then fed into a 1-dimensional convolutional neural network (1D-CNN) to train a deep learning model for the acoustic event classification. In addition, the dropout regularization is leveraged in the 1D-CNN to mitigate the overfitting problems. The proposed compressed sensing with 1D-CNN was evaluated on three benchmark datasets, namely Soundscapes1, Soundscapes2, and UrbanSound8K, and achieved F1-scores of 80.5%, 81.1%, and 69.2%, respectively.
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Acknowledgements
Authors would like to thank Collaborative Resea-rch in Engineering, Science and Technology Center (CREST) for their continuous support in this research. This research is supported by Collaborative Research in Engineering Science and Technology (CREST) R &D Grant No. CREST/R &D/P07C2-17/005 and Multimedia University Internal Research Fund MMUI/220028.
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Tan, P.S., Lim, K.M., Tan, C.H. et al. ComSense-CNN: acoustic event classification via 1D convolutional neural network with compressed sensing. SIViP 17, 735–741 (2023). https://doi.org/10.1007/s11760-022-02281-5
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DOI: https://doi.org/10.1007/s11760-022-02281-5