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

2 Citaten (Scopus)

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 29th International Joint Conference on Artificial Intelligence, IJCAI 2020
RedacteurenChristian Bessiere
UitgeverijInternational Joint Conferences on Artificial Intelligence (IJCAI)
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, IJCAI 2020, PRICAI 2020 - 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, IJCAI 2020, PRICAI 2020
Verkorte titelIJCAI-PRICAI 2020
Land/RegioJapan
StadYokohama
Periode11/07/2017/07/20
Internet adres

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