RoboJam: A musical mixture density network for collaborative touchscreen interaction

Charles Patrick Martin*, Jim Torresen

*Corresponding author for this work

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

14 Citations (Scopus)

Abstract

RoboJam is a machine-learning system for generating music that assists users of a touchscreen music app by performing responses to their short improvisations. This system uses a recurrent artificial neural network to generate sequences of touchscreen interactions and absolute timings, rather than high-level musical notes. To accomplish this, RoboJam’s network uses a mixture density layer to predict appropriate touch interaction locations in space and time. In this paper, we describe the design and implementation of RoboJam’s network and how it has been integrated into a touchscreen music app. A preliminary evaluation analyses the system in terms of training, musical generation and user interaction.

Original languageEnglish
Title of host publicationComputational Intelligence in Music, Sound, Art and Design - 7th International Conference, EvoMUSART 2018, Proceedings
EditorsAniko Ekart, Antonios Liapis, Juan Jesus Romero Cardalda
PublisherSpringer Verlag
Pages161-176
Number of pages16
ISBN (Print)9783319775821
DOIs
Publication statusPublished - 2018
Externally publishedYes
Event7th International Conference on Computational Intelligence in Music, Sound, Art and Design, EvoMUSART 2018 - Parma, Italy
Duration: 4 Apr 20186 Apr 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10783 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Conference on Computational Intelligence in Music, Sound, Art and Design, EvoMUSART 2018
Country/TerritoryItaly
CityParma
Period4/04/186/04/18

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