Year: 2016
Type: conference-abstract
Abstract: We introduced the use of modified DCTNet for audio signals feature extraction. The modified DCTNet is a development of DCTNet, with its center frequencies of filterbanks geometrically spaced. The modified DCTNet is adaptive to different acoustic scales, and it can better capture low frequency acoustic information that is sensitive to human audio perception. We use features extracted by the modified DCTNet and put them to classifiers. Experimental results on bird song classification, music genre classification, and artist identification show that the modified DCTNet and RNN improve classification rate, and achieve state of the art performance.
DOI: 10.1121/1.4970932
Authors: Yin Xian, Yunchen Pu, Zhe Gan, Liang Lu, Andrew Thompson
Institutions: Duke University, University of Edinburgh, University of Oxford
Language: English
Topic: Music and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Hausdorff Center for Mathematics
OpenAlex ID: W2764338498