Adaptive DCTNet for audio signal classification
Yin Xian, Yunchen Pu, Zhe Gan, Liang Lu, Andrew Thompson
Abstract
In this paper, we investigate DCTNet for audio signal classification. Its output feature is related to Cohen's class of time-frequency distributions. We introduce the use of adaptive DCTNet (A-DCTNet) for audio signals feature extraction. The A-DCTNet applies the idea of constant-Q transform, with its center frequencies of filterbanks geometrically spaced. The A-DCTNet is adaptive to different acoustic scales, and it can better capture low frequency acoustic information that is sensitive to human audio perception than features such as Mel-frequency spectral coefficients (MFSC). We use features extracted by the A-DCTNet as input for classifiers. Experimental results show that the A-DCTNet and Recurrent Neural Networks (RNN) achieve state-of-the-art performance in bird song classification rate, and improve artist identification accuracy in music data. They demonstrate A-DCTNet's applicability to signal processing problems.
BibTeX
@inproceedings{icassp2017_adaptivedctnetfo,
title = {Adaptive DCTNet for audio signal classification},
author = {Yin Xian and Yunchen Pu and Zhe Gan and Liang Lu and Andrew Thompson},
booktitle = {ICASSP 2017},
year = {2017}
}