AI Based Music Tunesmith

DOI

No DOI

Published In

Volume 4, Issue 3 (2023)

Published Online

May-June 2023

Pages

552-554

Article Type

Research Article

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Abstract: Generating music has some distinct differences from generating images and videos. To begin with, it is an art of time, so a temporal model is essential. Additionally, music is often composed of multiple instruments/tracks with their own tempo, which all unfold over time in a mutually dependent manner. Musical notes are also often grouped into chords, arpeggios, or melodies in polyphonic music, thus introducing a hierarchical ordering of notes. In this paper, we propose three models for symbolic multi-track music generation based on a framework of generative adversarial networks (GANS). The three models differ in their underlying assumptions and corresponding network architectures, and are referred to as the jamming model, the composer model, and the hybrid model. We trained the proposed models on a dataset of more than one hundred thousand bars of rock music, and applied them to generate piano rolls.References 1. Boulanger-Lewandowski, N., Bengio, Y., & Vincent, P. (2012). Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription. arXiv preprint arXiv:1206.6392. 2. Briot, J. P., Hadjeres, G., &Pachet, F. D. (2017). Deep learning techniques for music generation — a survey. arXiv preprint arXiv:1709.01620. 3. Choi, K., Fazekas, G., & Sandler, M. (2016). Text-based LSTM networks for automatic music composition. arXiv preprint arXiv:1604.05358. 4. Dong, H. W., Hsiao, W. Y., Yang, L. C., & Yang, Y. H. (2017). MuseGAN: Demonstration of a convolutional GAN based model for generating multi-track piano-rolls. ISMIR Late Breaking/Demos. 5. Eck, D., &Schmidhuber, J. (2002). A first look at music composition using lstm recurrent neural networks. IstitutoDalleMolle Di StudiSullIntelligenzaArtificiale, 103, 48. 6. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., … &Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672–2680). 7. Hadjeres, G., Pachet, F., & Nielsen, F. (2017, July). Deepbach: a steerable model for bach chorales generation. In International Conference on Machine Learning (pp. 1362–1371). PMLR. 8. Hochreiter, S., &Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735–1780. 9. Moorer, J. A. (1972). Music and computer composition. Communications of the ACM, 15 (2), 104–113. 47 10. A. Nefian and M. Hayes, ” An embedded hmm-based approach for face detection and recognition,” In Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 6, pp. 3553–3556, 1999. 11. U.S. Department of Defense, ”Facial Recognition Vendor Test, 2000,” Available: http://www.dodcounterdrug.com/facialrecognition/FRVT2000/frvt2000.htm 12. Banu, Danciu, Boboc, Moga, Balan; “A novel approach for face expression recognition”, IEEE 10th Jubilee International Symposium on Intelligent Systems and Informatics 2012. 13. Wang Zhen, Ying Zilu; “Facial expression recognition based on adaptive local binary pattern and sparse representation”, 2012 IEEE. 14. Deepti, Archana, Dr. Jagathy; “Facial expression recognition using ANN”, IOSR Journal of Computer Engineering 2013. Special Conference Issue: National Conference on Cloud Computing & Big Data 162. 15. Jizheng, Xia, Lijang, Yuli, Angelo; “Facial expression recognition considering differences in facial structure and texture”, IET Computer Vision 2013. 16. Jiawei, Congting, Hongyun, Zilu; “Facial expression recognition based on completed local binary pattern and sparse representation”, Ninth International Conference on Natural Computation (ICNC) 2013.