Pattern-Based Music Generation with Wasserstein Autoencoders and PRC Descriptions

V.A.J. (Tijn) Borghuis, Luca Angioloni, Lorenzo Brusci, Paolo Frasconi

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Samenvatting

We demonstrate a pattern-based MIDI music generation system with a generation strategy based on Wasserstein autoencoders and a novel variant of pianoroll descriptions of patterns which employs separate channels for note velocities and note durations and can be fed into classic DCGAN-style convolutional architectures. We trained the system on two new datasets (in the acid-jazz and high-pop genres) composed by musicians in our team with music generation in mind. Our demonstration shows that moving smoothly in the latent space allows us to generate meaningful sequences of four-bars patterns.
Originele taal-2Engels
TitelProceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
RedacteurenChristian Bessiere
Pagina's5225-5227
Aantal pagina's3
ISBN van elektronische versie9780999241165
DOI's
StatusGepubliceerd - jul 2020
Evenement29th International Joint Conference on Artificial Intelligence - 17th Pacific Rim International Conference on Artificial Intelligence. - Pacifico Convention Plaza Yokohama, Yokohama, Japan
Duur: 11 jul 202017 jul 2020
Congresnummer: 29
https://ijcai20.org/

Congres

Congres29th International Joint Conference on Artificial Intelligence - 17th Pacific Rim International Conference on Artificial Intelligence.
Verkorte titelIJCAI-PRICAI 2020
LandJapan
StadYokohama
Periode11/07/2017/07/20
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  • Citeer dit

    Borghuis, V. A. J. T., Angioloni, L., Brusci, L., & Frasconi, P. (2020). Pattern-Based Music Generation with Wasserstein Autoencoders and PRC Descriptions. In C. Bessiere (editor), Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (blz. 5225-5227) https://doi.org/10.24963/ijcai.2020/751