Pattern-Based Music Generation with Wasserstein Autoencoders and PRC Descriptions

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020
EditorsChristian Bessiere
PublisherInternational Joint Conference on Artificial Intelligence (IJCAI)
Pages5225-5227
Number of pages3
ISBN (Electronic)9780999241165
DOIs
Publication statusPublished - Jul 2020
Event29th International Joint Conference on Artificial Intelligence - 17th Pacific Rim International Conference on Artificial Intelligence. - Pacifico Convention Plaza Yokohama, Yokohama, Japan
Duration: 11 Jul 202017 Jul 2020
Conference number: 29
https://ijcai20.org/

Conference

Conference29th International Joint Conference on Artificial Intelligence - 17th Pacific Rim International Conference on Artificial Intelligence.
Abbreviated titleIJCAI-PRICAI 2020
CountryJapan
CityYokohama
Period11/07/2017/07/20
Internet address

Keywords

  • Machine learning
  • Human-Computer Interactive Systems
  • Music Generation

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