# Creative Prediction Projects Charles Martin - University of Oslo / Australian National University !(/assets/logo-bar.png)
## Supervisors !(/assets/people/charlesmartin.jpg) !(/assets/people/benediktewallace.jpg) **Charles Martin**. Lecturer in Computer Science, Australian National University. firstname.lastname@example.org **Benedikte Wallace**. PhD Researcher, University of Oslo. email@example.com
## Creative Predictions !(/assets/creative-prediction-image.png)
## Learning to Predict Sequences !(/assets/sequence-learning.png)
## Melody to Harmony in MicroJam !(/assets/robojam-interaction.png) 1. Gain an overview of DL for music generation. 2. Develop a melody to harmony sequence to sequence model 3. Train the model on matched melody/harmony sequences 4. Use MicroJam-sourced data as input and see if the generated harmonies make sense!
## Seq-to-Seq Music Generation !(https://magenta.tensorflow.org/assets/music_transformer/motifs_shaded_boxes.png) 1. Understand the Transformer architecture. 2. Implement your own Transformer (e.g., in Keras). 3. Find a musical dataset that could be trained. 4. Train your model, listen to the results and find a way to evaluate them.
## Generating colour palettes from audio data !(/assets/nainoa-shizuru-NcdG9mK3PBY-unsplash.jpg) 1. Gain an overview of DL for audio processing. 2. Obtain a dataset of audio and video (or colour) data. 3. Try different neural network designs and evaluate the results. (Even a simple fully-connected ANN might work well!)
## Motion-to-Motion Generators !(/assets/motion-to-motion.png) 1. Gain an overview of the main DL methods used for motion generation including RNNs, MDRNNs, and world models. 2. Find a dataset of motion capture or other movement data (or capture one yourself!) 3. Train the ANN and evaluate its generative abilities.