Fusion of Music Styles Using LSTM Recurrent Neural Networks
Switch branches/tags
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information.


Fusion of Music Styles Using LSTM Recurrent Neural Networks


This repo is the result of a collaboration between Jacob Sundstrom, Harsh Lal, Dave DeFillipo, and Nakul Tiruviluamala in a class entitled "Machine Learning and Music". We each expressed interest in extracting "musical features" from music and recombining them to form new, fused musical works. A paper was written and submitted.

The abstract is pasted below.


Appeal of a musical composition is almost exclusively subjective in that it is a combination of the tastes, preferences, and history of an individual's experiences. That is, it is perceived and judged qualitatively in a different way by different individuals. In this project we propose to build a deep learning system which could take n different samples of a jazz soloist - especially a variety of samples of specific 'styles' - and generate sound using current input as well as feedback and memory from the past samples. This generation can then be judged by a 'human' agent and the parameters of the neural network could be adjusted accordingly to generate a fusion music that is more closer and appealing to agent's expectations. Recurrent neural networks with Long Short Term Memory (LSTMs) in particular have shown promise as a module that can learn long songs sequences, and generate new compositions based on the song's harmonic structure and the feedback inherent in the network.


  • Jacob Sundstrom, Department of Music, UCSD
  • Harsh Lal, Computer Science and Engineering, UCSD