Fusion of Music Styles Using LSTM Recurrent Neural Networks
Authors: Jacob Sundstrom, Harsh Lal, Nakul Tiruviluamala, and David DeFilippo
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.