IJCAI 2020poster0 citations
Pattern-Based Music Generation with Wasserstein Autoencoders and PRC Descriptions
Valentijn Borghuis, Luca Angioloni, Lorenzo Brusci, Paolo Frasconi
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.
Machine Learning: generalHuman-Computer Interactive Systems: general
BibTeX
@inproceedings{ijcai2020p751,
title = {Pattern-Based Music Generation with Wasserstein Autoencoders and PRC Descriptions},
author = {Borghuis, Valentijn and Angioloni, Luca and Brusci, Lorenzo and Frasconi, Paolo},
booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
Artificial Intelligence, {IJCAI-20}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Christian Bessiere},
pages = {5225--5227},
year = {2020},
month = {7},
note = {Demos},
doi = {10.24963/ijcai.2020/751},
url = {https://doi.org/10.24963/ijcai.2020/751},
}