DLVS4Audio2Sheet: Deep Learning-Based Vocal Separation for Audio into Music Sheet Conversion

Author:

Teo NicoleORCID,Wang ZhaoxiaORCID,Ghe EzekielORCID,Tan Yee SenORCID,Oktavio KevanORCID,Lewi Alexander VincentORCID,Zhang AllyneORCID,Ho Seng-BengORCID

Publisher

Springer Nature Singapore

Reference28 articles.

1. Benetos, E., Dixon, S., Duan, Z., Ewert, S.: Automatic music transcription: an overview. IEEE Signal Process. Mag. 36(1), 20–30 (2018)

2. Chandna, P., Cuesta, H., Petermann, D., Gómez, E.: A deep-learning based framework for source separation, analysis, and synthesis of choral ensembles. Front. Signal Process. 2, 808594 (2022)

3. Cuesta, H., Gómez Gutiérrez, E., Martorell Domínguez, A., Loáiciga, F.: Analysis of intonation in unison choir singing. In: Proceedings of the 15th International Conference on Music Perception and Cognition / 10th Triennial Conference of the European Society for the Cognitive Sciences of Music, Graz (Austria), pp. 125–130 (2018)

4. Cuesta, H., M.B., Gómez, E.: Multiple f0 estimation in vocal ensembles using convolutional neural networks. arXiv preprint: arXiv:2009.04172 (2020)

5. Cuthbert, M.S., Ariza, C.: music21: a toolkit for computer-aided musicology and symbolic music data (2010)

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