1. Sequence to Sequence Music Generation
IN5490
Sequence to Sequence Music Generation
Torgrim R. Næss Synne Baumann Andrei Faitas

2. Main Objective
Generate a sequence of musical notes that harmonically match a given input sequence
Predictive Model

3. Methods Sequence to sequence RNN Bidirectional Evaluation
Temporal dependencies
Encoder-decoder LSTM
Bidirectional
Forward and backward dependencies
Evaluation
Qualitative
Quantitative

4. Training Data Four part MIDI music
Classical western music - they follow common rules and conventions
Augmentation:
Use well known composing techniques to generate more sequences
Transpose the pitch of the training sequence
Use different combinations of parts to create as many input/output pairs as possible
Section the music into shorter pieces

5. Experiments Still working on the code What we plan on exploring:
Effect of smaller datasets vs. larger augmented sets
Effect of “ordering” the training data
Compare bidirectional LSTM with standard unidirectional LSTM

6. Overall Experience The “Bad” The Good Steep learning curve
Time costly to familiarize ourselves with the tools needed
The Good
Appropriate level of freedom
The focus on producing a paper is a valuable experience