Attentional networks for music generation

• URL: http://arxiv.org/abs/2002.03854v1
• Date: Thu, 6 Feb 2020 13:26:17 GMT
• Title: Attentional networks for music generation
• Authors: Gullapalli Keerti, A N Vaishnavi, Prerana Mukherjee, A Sree Vidya, Gattineni Sai Sreenithya, Deeksha Nayab
• Abstract summary: We propose a deep learning based music generation method in order to produce old style music particularly JAZZ with rehashed melodic structures. 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.
• Score: 5.012960295592238
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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