RoboJam: A Musical Mixture Density Network for Collaborative Touchscreen Interaction
This addresses the problem of enhancing collaborative music-making for touchscreen app users, but it is incremental as it builds on existing neural network methods for music generation.
The authors tackled the problem of generating music in real-time to assist users of a touchscreen music app by developing RoboJam, a system that uses a recurrent neural network with a mixture density layer to predict touch interactions, resulting in a preliminary evaluation of its training and user interaction.
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.