Joint Detection and Classification of Singing Voice Melody Using Convolutional Recurrent Neural Networks

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<jats:p>Singing melody extraction essentially involves two tasks: one is detecting the activity of a singing voice in polyphonic music, and the other is estimating the pitch of a singing voice in the detected voiced segments. In this paper, we present a joint detection and classification (JDC) network that conducts the singing voice detection and the pitch estimation simultaneously. The JDC network is composed of the main network that predicts the pitch contours of the singing melody and an auxiliary network that facilitates the detection of the singing voice. The main network is built with a convolutional recurrent neural network with residual connections and predicts pitch labels that cover the vocal range with a high resolution, as well as non-voice status. The auxiliary network is trained to detect the singing voice using multi-level features shared from the main network. The two optimization processes are tied with a joint melody loss function. We evaluate the proposed model on multiple melody extraction and vocal detection datasets, including cross-dataset evaluation. The experiments demonstrate how the auxiliary network and the joint melody loss function improve the melody extraction performance. Furthermore, the results show that our method outperforms state-of-the-art algorithms on the datasets.</jats:p>
Publisher
MDPI
Issue Date
2019-03
Language
English
Article Type
Article
Citation

APPLIED SCIENCES-BASEL, v.9, no.7, pp.1324

ISSN
2076-3417
DOI
10.3390/app9071324
URI
http://hdl.handle.net/10203/262692
Appears in Collection
GCT-Journal Papers(저널논문)
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