The purpose of this project is to create a useful tool for musicians that utilizes the harmonic content of their playing to recommend new, relevant chords to play. This is done by training various Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs) on the lead sheets of 100 different jazz standards. A total of 200 unique datasets were produced and tested, resulting in the prediction of nearly 51 million chords. A note-prediction accuracy of 82.1% and a chord-prediction accuracy of 34.5% were achieved across all datasets. Methods of data representation that were rooted in valid music theory frameworks were found to increase the efficacy of harmonic prediction by up to 6%. Optimal LSTM input sizes were also determined for each method of data representation.
Details
- Data Representation for Predicting Harmonic Clusters with LSTM
- Rangaswami, Sriram Madhav (Author)
- Lalitha, Sankar (Thesis director)
- Jayasuriya, Suren (Committee member)
- Electrical Engineering Program (Contributor)
- Barrett, The Honors College (Contributor)