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ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) by Zhang, Liangqi (author);Luo, Yihao (author);Cao, Xiang (author);Shen, Haibo (author);Wang, Tianjiang (author) is a scholarly article available to read on EtoBox.

What is ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) about?

Convolutional neural networks (CNNs) have achieved superior performance but still lack clarity about the nature and properties of feature extraction. In this paper, by analyzing the sensitivity of neural networks to frequencies and scales, we find that neural networks not only have low-and mediumfrequency biases but also prefer different frequency bands for different classes, and the scale of objects influences the preferred frequency bands. These observations lead to the hypothesis that neural networks must learn the ability to extract features at various scales and frequencies. To corroborate this hypothesis, we propose a network architecture based on Gaussian derivatives, which extracts features by constructing scale space and employing partial derivatives as local feature extraction operators to separate high-frequency information. This manually designed method of extracting features from different scales allows our GSSDNets to achieve comparable accuracy with vanilla networks on various datasets.

Author

Zhang, Liangqi (author);Luo, Yihao (author);Cao, Xiang (author);Shen, Haibo (author);Wang, Tianjiang (author)

Publisher

IEEE

Published

2023

Language

EN