Fundamental Frequency Contour Classification: A Comparison between Hand-crafted and CNN-based Features

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(2019)

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摘要
In this paper, we evaluate hand-crafted features as well as features learned from data using a convolutional neural network (CNN) for different fundamental frequency classification tasks. We compare classification based on full (variable-length) contours and classification based on fixed-sized subcontours in combination with a fusion strategy. Our results show that hand-crafted and learned features lead to comparable results for both classification scenarios. Aggregating contour-level to file-level classification results generally improves the results. In comparison to the hand-crafted features, our examination indicates that the CNN-based features show a higher degree of redundancy across feature dimensions, where multiple filters (convolution kernels) specialize on similar contour shapes.
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关键词
Feature extraction,Task analysis,Music,Instruments,Frequency modulation,Shape
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