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Attentional networks for music generation

  • 1193: Intelligent Processing of Multimedia Signals
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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 serves as a 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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Notes

  1. http://web.mit.edu/music21/doc/moduleReference/moduleConverter.html

  2. https://www.kaggle.com/saikayala/jazz-ml-ready-midi

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Authors and Affiliations

  1. Indian Institute of Information Technology, Sri City, Andhra Pradesh, India

    Gullapalli Keerti, A N Vaishnavi, A Sree Vidya, Gattineni Sai Sreenithya & Deeksha Nayab

  2. Jawaharlal Nehru University, Delhi, India

    Prerana Mukherjee

Authors
  1. Gullapalli Keerti
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  2. A N Vaishnavi
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  3. Prerana Mukherjee
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  4. A Sree Vidya
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  5. Gattineni Sai Sreenithya
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  6. Deeksha Nayab
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Correspondence to Prerana Mukherjee.

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Keerti, G., Vaishnavi, A.N., Mukherjee, P. et al. Attentional networks for music generation. Multimed Tools Appl 81, 5179–5189 (2022). https://doi.org/10.1007/s11042-021-11881-1

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  • DOI: https://doi.org/10.1007/s11042-021-11881-1

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