A Deep Learning Approach for Generating Pleasant Music

Authors

  • Dax Jain  Inferenz Pvt. Ltd., Ahmedabad, Gujarat, India
  • Diya Mistry  Inferenz Pvt. Ltd., Ahmedabad, Gujarat, India
  • Dr. Nidhi Arora  Inferenz Pvt. Ltd., Ahmedabad, Gujarat, India

DOI:

https://doi.org//10.32628/CSEIT2173145

Keywords:

Music Generation, Deep Learning, LSTM, RNN, Polyphonic Music, MIDI

Abstract

Advancement in deep neural networks have made it possible to compose music that mimics music composition by humans. The capacity of deep learning architectures in learning musical style from arbitrary musical corpora have been explored in this paper. The paper proposes a method for generated from the estimated distribution. Musical chords have been extracted for various instruments to train a sequential model to generate the polyphonic music on some selected instruments. We demonstrate a simple method comprising a sequential LSTM models to generate polyphonic music. The results of the model evaluation show that generated music is pleasant to hear and is similar to music played by humans. This has great application in entertainment industry which enables music composers to generate variety of creative music.

References

  1. Huang, A. & Wu, R. (2016). Deep Learning for Music. CoRR, abs/1606.04930.
  2. Oord, A. van den, Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A. W., & Kavukcuoglu, K. (2016). WaveNet: A Generative Model for Raw Audio. In The 9th ISCA Speech Synthesis Workshop, Sunnyvale, CA, USA, 13-15 September 2016 (pp. 125). ISCA.
  3. Blacking, J. (1969). The value of music in human experience. Year Book of the International Folk Music Council, pp. 33-71.
  4. Boenn, G., Brain, M., De vos, M., & Ffitch, J. (2011). Automatic Music Composition Using Answer Set Programming. Theory Pract. Log. Program., 11(2–3), 397–427.
  5. Boulanger-Lewandowski, N., Bengio, Y., & Vincent, P. (2012). Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription. In Proceedings of the 29th International Coference on International Conference on Machine Learning (pp. 1881–1888). Omnipress.
  6. Chuan, Ching-Hua, & Herremans, D., (2018). Modeling Temporal Tonal Relations in Polyphonic Music Through Deep Networks with a Novel Image-Based Representation. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018 (pp. 2159–2166). AAAI Press.
  7. Chun-chi J. Chen, & Miikkulainen, R., (2001). Creating Melodies with Evolving Recurrent Neural Networks. In in: Proceedings of the INNS-IEEE International Joint Conference on Neural Networks, 2241–2246, IEEE, Piscataway, NJ (pp. 2241–2246).
  8. Correa, D., Levada, A., Saito, J., & Mari, J. (2008). Neural Network Based Systems for Computer-Aided Musical Composition: Supervised x Unsupervised Learning. In Proceedings of the 2008 ACM Symposium on Applied Computing (pp. 1738–1742). Association for Computing Machinery.
  9. Eck, D., Schmidhuber, J. (2002). A First Look at Music Composition using LSTM Recurrent Neural Networks. Istituto Dalle Molle Di Studi Sull Intelligenza Artificiale.
  10. Franklin, J. (2006). Recurrent Neural Networks for Music Computation. INFORMS J. on Computing, 18(3), 321–338.
  11. Brunner, G., Wang, Y., Wattenhofer, R. & Wiesendanger, J. (2017). JamBot: Music Theory Aware Chord Based Generation of Polyphonic Music with LSTMs. In 29th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2017, Boston, MA, USA, November 6-8, 2017 (pp. 519–526). IEEE Computer Society.
  12. Chu, H., Urtasun, R. & Fidler, S. (2017). Song From PI: A Musically Plausible Network for Pop Music Generation. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Workshop Track Proceedings. OpenReview.net.
  13. Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), pp. 1735–1780.
  14. Choi, K., & Fazekas, G. & Sandler, M.B. (2016). Text-based LSTM networks for Automatic Music Composition. CoRR, abs/1604.05358.
  15. Lyu, Qi, Wu, Z., Zhu, J. & Meng. H. (2015). Modelling High-Dimensional Sequences with LSTM-RTRBM: Application to Polyphonic Music Generation. Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, pp. 4138-4139.
  16. Xenakis, I. (1971). Formalized Music Thought and Mathematics in Composition. Indiana University Press.
  17. Liu, I-Ting & Ramakrishnan, B. (2014). Bach in 2014: Music Composition with Recurrent Neural Network. CoRR, abs/1412.3191.
  18. Briot, J.P., Hadjeres, G. & Pachet, F.D. (2017). Deep Learning Techniques for Music Generation - A Survey. CoRR, abs/1709.01620.
  19. Kim, S., & André, E. (2004). A Generate and Sense Approach to Automated Music Composition. In Proceedings of the 9th International Conference on Intelligent User Interfaces (pp. 268–270). Association for Computing Machinery.
  20. Raffel, C. (2016). Learning-Based Methods for Comparing Sequences, with Applications to Audio-to-MIDI Alignment and Matching. Columbia University.
  21. Salas, H., Gelbukh, A., & Calvo, H. (2010). Music Composition Based on Linguistic Approach. In Proceedings of the 9th Mexican International Conference on Advances in Artificial Intelligence: Part I (pp. 117–128). Springer-Verlag.
  22. Sigtia, S., Benetos, E., & Dixon, S. 2016. An end-to-end neural network for polyphonic piano music transcription. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) 24(5):927–939.
  23. Mehri, S., Kumar, K., Gulrajani, I., Kumar, R., Jain, S., Sotelo, J., Courville, A.C. & Bengio, Y. (2016). SampleRNN: An Unconditional End-to-End Neural Audio Generation Model. CoRR, abs/1612.07837.
  24. Unehara, M., & Onisawa, T. (2005). Music Composition by Interaction between Human and Computer. New Gen. Comput., 23(2), 181–191.
  25. Wells, D., & ElAarag, H. (2011). A Novel Approach for Automated Music Composition Using Memetic Algorithms. In Proceedings of the 49th Annual Southeast Regional Conference (pp. 155–159). Association for Computing Machinery.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2021-06-30

Issue

Volume 7, Issue 3, May-June-2021

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Dax Jain, Diya Mistry, Dr. Nidhi Arora, " A Deep Learning Approach for Generating Pleasant Music, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 3, pp.641-649, May-June-2021. Available at doi : https://doi.org/10.32628/CSEIT2173145