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arXiv · 2002.03854

Attentional networks for music generation

Gullapalli Keerti·A N Vaishnavi·Prerana Mukherjee·A Sree Vidya·Gattineni Sai Sreenithya·Deeksha Nayab
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Abstract

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.

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Gullapalli Keerti, A N Vaishnavi, Prerana Mukherjee, A Sree Vidya, Gattineni Sai Sreenithya, Deeksha Nayab. 2020-02-06. Attentional networks for music generation. https://arxiv.org/abs/2002.03854

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