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Journal Article
Source Separation Using Dilated Time-Frequency DenseNet for Music Identification in Broadcast Contents
- Authors
- Woon-Haeng Heo, Hyemi Kim, Oh-Wook Kwon
- Issue Date
- 2020-03
- Citation
- Applied Sciences, v.10, no.5, pp.1-18
- ISSN
- 2076-3417
- Publisher
- MDPI
- Language
- English
- Type
- Journal Article
- Abstract
- 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.
- KSP Keywords
- Background music, Best performance, Contextual information, Different frequency, Dilated Convolution, Frequency characteristics, High frequency(HF), Music identification, Receptive field, frequency band, mixed signal
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
