Abstract
How to effectively and accurately identify the sound event in a real-world noisy environment is still a challenging problem. Traditional methods for robust sound event classification generally perform well in clean conditions, but get worse in noisy situations. Biological evidence shows that local temporal and spectral information can be utilized for processing noise corrupted signals, motivating our novel approach for sound recognition by combining this with a convolutional neural network (CNN), one of the most popularly applied methods in acoustic processing. We use key-points (KPs) to construct a robust and sparse representation of the sound, followed by a CNN being trained as a classifier. RWCP database is used to evaluate the performance of our system. Our results show that the as-proposed KP-CNN system is effective and efficient for a robust sound event classification task in both mismatched and multi-condition environments.
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Acknowledgements
This work was supported by the Natural Science Foundation of China (No. 61806139, 61771333), and the Natural Science Foundation of Tianjin (No. 18JCYBJC41700).
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Yao, Y., Yu, Q., Wang, L., Dang, J. (2019). Robust Sound Event Classification with Local Time-Frequency Information and Convolutional Neural Networks. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series. ICANN 2019. Lecture Notes in Computer Science(), vol 11730. Springer, Cham. https://doi.org/10.1007/978-3-030-30490-4_29
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