A Lightweight Music Source Separation Model with Graph Convolution Network

Author:

Zhu MengyingORCID,Wang LiusongORCID,Hu YingORCID

Publisher

Springer Nature Singapore

Reference41 articles.

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2. Rosner, A., Kostek, B., Schuller, B.: Classification of music genres based on music separation into harmonic and drum components. In: Archives of Acoustics, pp. 629–638 (2014)

3. Dittmar, C., Cano, E., Abeßer, J., Grollmisch, S.: Music information retrieval meets music education. In: Dagstuhl Follow-Ups, vol. 3. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik (2012)

4. Reimer, B.: A Philosophy of Music Education: Advancing the Vision. State University of New York Press, New York (2022)

5. Luo, Y., Mesgarani, N.: Conv-tasnet: surpassing ideal time-frequency magnitude masking for speech separation. IEEE/ACM Trans. Audio Speech Lang. Process. 27(8), 1256–1266 (2019)

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