The MDRNN is an exciting sequence model that can generate multiple continuous valued signals from a Gaussian mixture model at each step in time. This project will involve applying and extending my Keras MDRNN models into new applications in the creative arts and beyond. We’ve tried using the MDRNN for voice synthesis, motion capture data synthesis, and musical control data synthesis, but there are lots of other potential applications waiting for you to discover, e.g.: predicting future sensor values, generating robot movements, generating world models for video games or real life situations etc.
- gain an understanding of mixture density networks and artificial neural network designs
- apply the MDN to a new application area, this could be a creative arts application, sequence learning from real data, or some other application that you dream up!
- develop your system and train with real-world data
- Python programming
- completed coursework in machine learning or artificial intelligence
- motivation to work on a new problem and obtain a dataset for training and testing
mixture density network, machine learning, applications