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A Novel Dataset for the Identification of Computer Generated Melodies in the CSMT Challenge

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Proceedings of the 8th Conference on Sound and Music Technology (CSMT 2020)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 761))

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Abstract

This paper introduces a novel dataset for the identification of computer generated melodies as used in the data challenge organised by the Conference on Sound and Music Technology (CSMT). The CSMT data challenge requires participants to identify whether a given piece of melody is generated by computer or is composed by human. The dataset consists of two parts: a development dataset and an evaluation dataset. The development dataset contains only computer generated melodies whereas the evaluation dataset contain both computer generated melodies and human composed melodies. The aim of the dataset is to facilitate the develpment and assessment of methods to identified computer generated melodies and facilitate the creation of generative music systems.

Supported by Zhongwen Law Firm. Shengchen Li and Yinji Jing are considered joint first authors.

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Notes

  1. 1.

    https://web.mit.edu/music21/.

  2. 2.

    https://www.hooktheory.com/.

  3. 3.

    http://craffel.github.io/pretty-midi/.

References

  1. Ackerman, M., Loker, D.: Algorithmic songwriting with ALYSIA. In: International Conference on Evolutionary and Biologically Inspired Music and Art, Amsterdam, The Netherlands, pp. 1–16. Springer (2017)

    Google Scholar 

  2. Briot, J.P., Hadjeres, G., Pachet, F.D.: Deep Learning Techniques for Music Generation. Springer (2020)

    Google Scholar 

  3. Brunner, G., Wang, Y., Wattenhofer, R., Zhao, S.: Symbolic music genre transfer with CycleGAN. In: 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), Volos, Greece, pp. 786–793. IEEE (2018)

    Google Scholar 

  4. Cheng, T., Smith, J.B., Goto, M.: Music structure boundary detection and labelling by a deconvolution of path-enhanced self-similarity matrix. In: 2018 IEEE International Conference on Acoustics. Speech and Signal Processing (ICASSP), Calgary, Canada, pp. 106–110. IEEE (2018)

    Google Scholar 

  5. Dong, H.W., Hsiao, W.Y., Yang, L.C., Yang, Y.H.: MuseGAN: multi-track sequential generative adversarial networks for symbolic music generation and accompaniment. In: Thirty-Second AAAI Conference on Artificial Intelligence, Orlando, USA, pp. 34–41. AAAI (2018)

    Google Scholar 

  6. Eck, D., Schmidhuber, J.: A first look at music composition using LSTM recurrent neural networks. Istituto Dalle Molle Di Studi Sull Intelligenza Artificiale 103, 48 (2002)

    Google Scholar 

  7. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, Montreal, Canada, pp. 2672–2680 (2014)

    Google Scholar 

  8. Herremans, D., Chuan, C.H., Chew, E.: A functional taxonomy of music generation systems. ACM Comput. Surv. (CSUR) 50(5), 69 (2017)

    Article  Google Scholar 

  9. Huang, C.Z.A., Vaswani, A., Uszkoreit, J., Simon, I., Hawthorne, C., Shazeer, N., Dai, A.M., Hoffman, M.D., Dinculescu, M., Eck, D.: Music transformer: generating music with long-term structure. In: International Conference on Learning Representations, Vancouver, BC, Canada (2018)

    Google Scholar 

  10. Liu, C.H., Ting, C.K.: Computational intelligence in music composition: a survey. IEEE Trans. Emerg. Top. Comput. Intell. 1(1), 2–15 (2016)

    Article  Google Scholar 

  11. Liu, H.M., Yang, Y.H.: Lead sheet generation and arrangement by conditional generative adversarial network. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, USA, pp. 722–727. IEEE (2018)

    Google Scholar 

  12. Luo, J., Yang, X., Ji, S., Li, J.: MG-VAE: deep Chinese folk songs generation with specific regional styles. In: Proceedings of the 7th Conference on Sound and Music Technology (CSMT), Haerbin, China, pp. 93–106. Springer (2020)

    Google Scholar 

  13. Makris, D., Kaliakatsos-Papakostas, M., Karydis, I., Kermanidis, K.L.: Combining LSTM and feed forward neural networks for conditional rhythm composition. In: International Conference on Engineering Applications of Neural Networks, pp. 570–582. Springer (2017)

    Google Scholar 

  14. Pinkerton, R.C.: Information theory and melody. Sci. Am. 194(2), 77–87 (1956)

    Article  Google Scholar 

  15. Roberts, A., Engel, J., Raffel, C., Hawthorne, C., Eck, D.: A hierarchical latent vector model for learning long-term structure in music. In: Dy, J., Krause, A. (eds.) Proceedings of Machine Learning Research, Stockholmsmässan, Stockholm, Sweden, 10–15 July 2018, vol. 80, pp. 4364–4373. PMLR (2018)

    Google Scholar 

  16. Steedman, M.J.: A generative grammar for Jazz chord sequences. Music Percept. Interdiscip. J. 2(1), 52–77 (1984)

    Article  Google Scholar 

  17. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Advances in Neural Information Processing Systems, Long Beach, USA, pp. 5998–6008 (2017)

    Google Scholar 

  18. Wang, Y.A., Huang, Y.K., Lin, T.C., Su, S.Y., Chen, Y.N.: Modeling melodic feature dependency with modularized variational auto-encoder. In: 2019 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP) ICASSP 2019, Brighton, UK, pp. 191–195. IEEE (2019)

    Google Scholar 

  19. Yang, L.C., Chou, S.Y., Yang, Y.H.: MidiNet: a convolutional generative adversarial network for symbolic-domain music generation. In: 18th International Society for Music Information Retrieval Conference, Suzhou, China, pp. 324–331 (2017)

    Google Scholar 

  20. Zhu, H., Liu, Q., Yuan, N.J., Qin, C., Li, J., Zhang, K., Zhou, G., Wei, F., Xu, Y., Chen, E.: Xiaoice band: a melody and arrangement generation framework for pop music. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, United Kingdom, pp. 2837–2846. ACM (2018)

    Google Scholar 

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Acknowledgement

The authors acknowledge the contribution from all members in the organisation committee (other than the authors): Prof. ZHANG Ru from Beijing University of Posts and Telecommunications, Dr. LI Zijin from China Conservatory of Music, Mr. ZHU Yidan from Beijing Acoustics Society, Mr. ZHOU Wei from Beijing Zhongwen (Shanghai) Law Firm.

Author information

Authors and Affiliations

  1. Department of Intelligent Science, School of Advanced Technology, Xi’an Jiaotong-Liverpool University, 111 Ren’ai Road, Suzhou Industrial Park, Suzhou, 215123, Jiangsu Province, P. R. China

    Shengchen Li

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Yinji Jing

  3. Queen Mary University of London, London, UK

    György Fazekas

Authors
  1. Shengchen Li
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  2. Yinji Jing
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  3. György Fazekas
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Corresponding author

Correspondence to Shengchen Li .

Editor information

Editors and Affiliations

  1. Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China

    Xi Shao

  2. The University of Tokyo, Bunkyo-ku, Tokyo, Japan

    Kun Qian

  3. China University of Geosciences, Wuhan, Hubei, China

    Li Zhou

  4. Communication University of China, Beijing, China

    Xin Wang

  5. Tianjin Normal University, Tianjin, China

    Ziping Zhao

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Li, S., Jing, Y., Fazekas, G. (2021). A Novel Dataset for the Identification of Computer Generated Melodies in the CSMT Challenge. In: Shao, X., Qian, K., Zhou, L., Wang, X., Zhao, Z. (eds) Proceedings of the 8th Conference on Sound and Music Technology . CSMT 2020. Lecture Notes in Electrical Engineering, vol 761. Springer, Singapore. https://doi.org/10.1007/978-981-16-1649-5_15

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  • DOI: https://doi.org/10.1007/978-981-16-1649-5_15

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-1648-8

  • Online ISBN: 978-981-16-1649-5

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