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

Abstract

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.