@inproceedings{bb493f492ff047fbac39c0d9d4f17b19,
title = "RoboJam: A musical mixture density network for collaborative touchscreen interaction",
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{\textquoteright}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{\textquoteright}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.",
keywords = "Artificial neural networks, Collaboration, Intelligent agents, Mobile music, Musical artificial intelligence",
author = "Martin, {Charles Patrick} and Jim Torresen",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 7th International Conference on Computational Intelligence in Music, Sound, Art and Design, EvoMUSART 2018 ; Conference date: 04-04-2018 Through 06-04-2018",
year = "2018",
doi = "10.1007/978-3-319-77583-8_11",
language = "English",
isbn = "9783319775821",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "161--176",
editor = "Aniko Ekart and Antonios Liapis and {Romero Cardalda}, {Juan Jesus}",
booktitle = "Computational Intelligence in Music, Sound, Art and Design - 7th International Conference, EvoMUSART 2018, Proceedings",
address = "Germany",
}