Attentional networks for music generation
This work addresses music generation for specific styles like JAZZ, but it is incremental as it applies existing Bi-LSTM with attention methods to a new domain.
The authors tackled the problem of generating realistic music, specifically old-style JAZZ with rehashed melodic structures, by proposing a deep learning method using a Bi-LSTM Neural Network with Attention, and they validated that this approach preserves the richness and technical nuances of the music.
Realistic music generation has always remained as a challenging problem as it may lack structure or rationality. In this work, we propose a deep learning based music generation method in order to produce old style music particularly JAZZ with rehashed melodic structures utilizing a Bi-directional Long Short Term Memory (Bi-LSTM) Neural Network with Attention. Owing to the success in modelling long-term temporal dependencies in sequential data and its success in case of videos, Bi-LSTMs with attention serve as the natural choice and early utilization in music generation. We validate in our experiments that Bi-LSTMs with attention are able to preserve the richness and technical nuances of the music performed.