Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)

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Automatic Music Generation Using Deep Learning

Authors
Ratika Jadhav1, Aarati Mohite2, Debashish Chakravarty2, Sanjay Nalbalwar1, *
1Electronics and Telecommunications, Dr. Babasaheb, Ambedkar Technological University, Lonere Raigad, India
2Electronics and Telecommunications, Indian Institute of Technology Kharagpur, Kharagpur, India
*Corresponding author. Email: nalbalwar_sanjayan@yahoo.com
Corresponding Author
Sanjay Nalbalwar
Available Online 10 August 2023.
DOI
10.2991/978-94-6463-196-8_51How to use a DOI?
Keywords
Music Generation; Neural Network; RNN; LSTM; Deep Learning; ABC Notation; Duration; Frequency
Abstract

This paper aims to build an automatic music generation model for generating musical sequences in ABC notation using a multi-layer Long Short-Term Memory (LSTM) neural network. The model is trained on polyphony such as piano folk and old Scottish flute, merged with various ABC notation tunes by five composers, viz., Nottingham, Jack Campin, Rachael Rae, Quin Abbey, and Rabbie Burns. This approach inputs an arbitrary note from each of the five merged datasets into the neural networks. Depending on the input note, the sequence can process and enlarge until a tune of descent music is generated. With the help of hyperparameter optimization, 95% accuracy is achieved. The model's output efficiency is evaluated using frequency, autocorrelation, PSD, noise filtering, and spectrum analysis. The results show that expressive elements like duration, pitch, and harmony are essential aspects of music composition, and progress has been made to improve these parameters. In addition, the generated note frequency of music is C# (sharp), which evokes sentiments of peace and happiness in mind.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)
Series
Advances in Intelligent Systems Research
Publication Date
10 August 2023
ISBN
10.2991/978-94-6463-196-8_51
ISSN
1951-6851
DOI
10.2991/978-94-6463-196-8_51How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Ratika Jadhav
AU  - Aarati Mohite
AU  - Debashish Chakravarty
AU  - Sanjay Nalbalwar
PY  - 2023
DA  - 2023/08/10
TI  - Automatic Music Generation Using Deep Learning
BT  - Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022)
PB  - Atlantis Press
SP  - 674
EP  - 685
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-196-8_51
DO  - 10.2991/978-94-6463-196-8_51
ID  - Jadhav2023
ER  -