Interweaving and Enriching Digital Music Collections for Scholarship, Performance, and Enjoyment
Author:
Affiliation:
1. University of Music and Performing Arts Vienna
2. Goldsmiths, University of London
3. Universitat Pompeu Fabra
4. Muziekweb
5. Delft University of Technology
6. Voctro Labs
7. Royal Concertgebouw Orchestra
Funder
Horizon 2020
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3358664.3358666
Reference28 articles.
1. Merlijn Blaauw and Jordi Bonada. 2017. A Neural Parametric Singing Synthesizer Modeling Timbre and Expression from Natural Songs. Applied Sciences 7 12 (2017). https://doi.org/10.3390/app7121313 Merlijn Blaauw and Jordi Bonada. 2017. A Neural Parametric Singing Synthesizer Modeling Timbre and Expression from Natural Songs. Applied Sciences 7 12 (2017). https://doi.org/10.3390/app7121313
2. Merlijn Blaauw Jordi Bonada and Ryunosuke Daido. 2019. Data Efficient Voice Cloning for Neural Singing Synthesis. CoRR abs/1902.07292(2019). arxiv:1902.07292http://arxiv.org/abs/1902.07292 Merlijn Blaauw Jordi Bonada and Ryunosuke Daido. 2019. Data Efficient Voice Cloning for Neural Singing Synthesis. CoRR abs/1902.07292(2019). arxiv:1902.07292http://arxiv.org/abs/1902.07292
3. Gianna Cassidy and Raymond MacDonald. 2009. The effects of music choice on task performance: A study of the impact of self-selected and experimenter-selected music on driving game performance and experience. Musicae Scientiae (2009). https://doi.org/10.1177/102986490901300207 Gianna Cassidy and Raymond MacDonald. 2009. The effects of music choice on task performance: A study of the impact of self-selected and experimenter-selected music on driving game performance and experience. Musicae Scientiae (2009). https://doi.org/10.1177/102986490901300207
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