期刊
APPLIED SCIENCES-BASEL
卷 10, 期 5, 页码 -出版社
MDPI
DOI: 10.3390/app10051727
关键词
source separation; DenseNet; broadcast contents; music identification; dilated convolution
类别
资金
- Ministry of Culture, Sports and Tourism (MCST)
- Korea Copyright Commission
- Korea Creative Content Agency (KOCCA) [2018-MICRO-9500] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Foundation of Korea [21A20131600002] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
We propose a source separation architecture using dilated time-frequency DenseNet for background music identification of broadcast content. We apply source separation techniques to the mixed signals of music and speech. For the source separation purpose, we propose a new architecture to add a time-frequency dilated convolution to the conventional DenseNet in order to effectively increase the receptive field in the source separation scheme. In addition, we apply different convolutions to each frequency band of the spectrogram in order to reflect the different frequency characteristics of the low- and high-frequency bands. To verify the performance of the proposed architecture, we perform singing-voice separation and music-identification experiments. As a result, we confirm that the proposed architecture produces the best performance in both experiments because it uses the dilated convolution to reflect wide contextual information.
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