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Research Article
AI Based Music Tunesmith
Amritenra Pratap Singh1
Shobhit Sharma2
Amn Singh3
Ramesh Vaish4
Anamika Yadav5
1235students, Babubanarsi Das institute of technology and management, LuckNow, Uttar Pradesh, India. 4Assistant professor, Babubanarsi Das institute of technology and management, LuckNow, Uttar Pradesh, India.
Published Online: May-June 2023
Pages: 552-554
Cite this article
No DOIReferences
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Conference on Machine Learning (pp. 1362–1371). PMLR.
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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.
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