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Research Article

Neural Networks And Ensemble Based Architectures To Automatic Musical Harmonization: A Performance Comparison

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References

  • Artstein, R., and M. Poesio. 2008. Inter-coder agreement for computational linguistics. Computational Linguistics 34 (4):555–859. dec. https://direct.mit.edu/coli/article/34/4/555-596/1999. Retrieved from.
  • Belotti, J., H. Siqueira, L. Araujo, S. L. Stevan, P. S. de Mattos Neto, M. H. Marinho, J. F. L. de Oliveira, F. Usberti, M. D. A. Leone Filho, and A. Converti. (2020). Neural-based ensembles and unorganized machines to predict streamflow series from hydroelectric plants. Energies 13 (18):4769. doi:10.3390/en13184769.
  • Branco, P., L. Torgo, and R. Ribeiro (2015, may). A survey of predictive modelling under imbalanced distributions. Retrieved from http://arxiv.org/abs/1505.01658
  • Chuan, C. (2011). A comparison of statistical and rule-based models for style-specific harmonization. In International Society for Music Information Retrieval Conference, Miami, Florida, USA, (pp. 221–26).
  • Cohen, J. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20 (1):37–46. apr. Retrieved from http://journals.sagepub.com/doi/10.1177/001316446002000104.
  • Costa, L. F. P., A. Y. C. Ogoshi, M. S. R. Martins, and H. V. Siqueira (2022). Developing a measure image and applying to deep learning. In Music encoding conference. Halifax, CA: Music Encoding Initiative.
  • Cramer, H. 1962. Mathematical methods of statistics. 1st ed. Bombay: Asia Publishing House.
  • de Souza Tadano, Y., H. Siqueira, and T. Alves (2016). Unorganized machines to predict hospital admissions for respiratory diseases. In Latin american conference on computational intelligence, Cartagena, Colombia, (pp. 1–6).
  • Dong, H. -W., W. -Y. Hsiao, L. -C. Yang, and Y. -H. Yang (2017). MuseGAN: Multi-track sequential generative adversarial networks for symbolic music generation and accompaniment. In AAAI conference on artificial intelligence, San Francisco, California, USA, (pp. 34–41).
  • Ebcioğlu, K. 1988. An expert four-part harmonizing chorales. Computer Music Journal 12 (3):43–51. doi:10.2307/3680335.
  • Friedman, M. 1937. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association 32 (200):675–701. doi:10.1080/01621459.1937.10503522.
  • Gómez, R. (2018). Understanding Categorical Cross-Entropy Loss, Binary Cross-Entropy Loss, Softmax Loss, Logistic Loss, Focal Loss and all those confusing names. Retrieved 2021-11-02, from https://gombru.github.io/2018/05/23/cross entropy loss/
  • Haykin, S. 2008. Neural Networks and learning machines. 3rd ed. Hamilton, ON: Prentice Hall.
  • Hochreiter, S., and J. Schmidhuber (1996). Lstm can solve hard long time lag problems. In Proceedings of the 9th international conference on neural information processing systems (p. 473–79). Cambridge, MA, USA: MIT Press.
  • Hochreiter, S., and J. Schmidhuber. 1997, nov. Long short-term memory. ( Retrieved from) Neural Computation 9 (8):1735–80. doi: 10.1162/neco.1997.9.8.1735.
  • Huang, A., and R. Wu. (2016). Deep learning for music. Deep Learning for Natural Language Processing. doi:10.48550/ARXIV.1606.04930.
  • Huang, G. -B., Q. -Y. Zhu, and C. -K. Siew. 2006. Extreme learning machine: Theory and applications. Neurocomputing 70 (1–3):489–501. doi:10.1016/j.neucom.2005.12.126.
  • Jaeger, H. (2010). The “echo state” approach to analysing and training recurrent neural networks – with an Erratum note (Tech. Rep. No. 148). GMD - German National Research Institute for Computer Science.
  • Kachba, Y., D. Chiroli, J. Belotti, T. Alves, Y. de Souza Tadano, and H. Siqueira. 2020. Artificial neural networks to estimate the influence of vehicular emission variables on morbidity and mortality in the largest metropolis in south america. Sustainability 12 (7):2621. doi:10.3390/su12072621.
  • Koops, H., M. Pedro, and W. de Haas (2013). A functional approach to automatic melody harmonisation. In Acm sigplan international conference on functional programming, Boston, Massachusetts, USA, (p. 47–58).
  • Lim, H., and K. Lee (2017). Chord generation from symbolic melody using BLSTM networks. In International society for music information retrieval conference, Suzhou, China, (pp. 621–27).
  • Lim, H., S. Rhyu, and K. Lee (2017). CSV Leadsheet Database. Retrieved 2019-06-08, from http://marg.snu.ac.kr/chordgeneration/
  • Liu, H. -M., and Y. -H. Yang (2018). Lead sheet generation and arrangement by conditional generative adversarial network. In International conference on machine learning and applications, Paris, France, (pp. 722–27). IEEE.
  • Miller, N. (2006). Heritage and Innovation of Harmony: A Study of West Side Story ( Unpublished doctoral dissertation). University of North Texas.
  • Nakashima, S., Y. Imamura, S. Ogawa, and M. Fukumoto (2010). Generation of appropriate user chord development based on interactive genetic algorithm. In International conference on p2p, parallel, grid, cloud and internet computing, Fukuoka, Japan, (p. 450–53).
  • Neto, P. S. D. M., P. R. A. Firmino, H. Siqueira, Y. D. S. Tadano, T. A. Alves, J. F. L. De Oliveira, M. H. D. N. Madeiro, and F. Madeiro. (2021). Neural-based ensembles for particulate matter forecasting. IEEE Access 9:14470–90. doi:10.1109/ACCESS.2021.3050437.
  • Piotrowski Pawełand Baczyński, D., M. Gulczyński, T. Kopyt, and T. Gulczyński. 2022. Advanced ensemble methods using machine learning and deep learning for one-day-ahead forecasts of electric energy production in wind farms. Energies 15 (4):1252. doi:10.3390/en15041252.
  • Powers, D. M. W. (2007). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation (Tech. Rep). Adelaide: Flinders University. Retrieved from http://arxiv.org/abs/2010.16061
  • Rhyu, S., H. Choi, S. Kim, and K. Lee. 2022. Translating melody to chord: Structured and flexible harmonization of melody with transformer. IEEE Access 10:28261–73. 02. doi: 10.1109/ACCESS.2022.3155467.
  • Ribeiro, V., G. Reynoso-Meza, and H. Siqueira. 2020. Multi-objective ensembles of echo state networks and extreme learning machines for streamflow series forecasting. Engineering Applications of Artificial Intelligence 95:103910. doi:10.1016/j.engappai.2020.103910.
  • Roig-Francolí, M. 2010. Harmony in Context. 2nd ed. Cincinnati, OH: McGraw-Hill.
  • Schoenberg, A. 1978. Theory of Harmony. 1st ed. Berkeley, CA: University of California Press.
  • Siqueira, H., L. Boccato, R. Attux, and C. Lyra Filho (2012a). Echo state networks for seasonal streamflow series forecasting. In International conference on intelligent data engineering and automated learning, Natal, Brazil, (pp. 226–36).
  • Siqueira, H., L. Boccato, R. Attux, and C. Lyra Filho. 2012b. Echo state networks in seasonal streamflow series prediction. Learning and Nonlinear Models 10:181–91. doi:10.21528/LNLM-vol10-no3-art5.
  • Siqueira, H., and I. Luna. 2019. Performance comparison of feedforward neural networks applied to streamflow series forecasting. Mathematics in Engineering Science and Aerospace 10 (1):41–53.
  • Siqueira, H., M. Macedo, Y. D. S. Tadano, T. A. Alves, S. L. Stevan Jr, D. S. Oliveira Jr, M. H. N. Marinho, P. S. G. D. M. Neto, J. F. L. D. Oliveira, I. Luna, et al. 2020. … others. Energies 13 (16):4236. doi:10.3390/en13164236.
  • Tadano, Y. S., S. Potgieter-Vermaak, Y. R. Kachba, D. M. Chiroli, L. Casacio, J. C. Santos-Silva, C. A. B. Moreira, V. Machado, T. A. Alves, H. Siqueira, et al. 2021. Dynamic model to predict the association between air quality, COVID-19 cases, and level of lockdown. Environmental Pollution 268:115920. doi:10.1016/j.envpol.2020.115920.
  • Terefenko, D. 2014. Jazz theory: From basic to advanced study. 1st ed. Rochester, NY: Routledge.
  • Wichard, J. D., and M. Ogorzalek (2004). Time series prediction with ensemble models. In International joint conference on neural networks, Shenzhen, China, (Vol. 2, pp. 1625–30). IEEE.
  • Wiggins, G., G. Papadopoulos, and S. Phon-Amnuaisuk. 1998. Evolutionary methods for musical composition. International Journal of Computing Anticipatory Systems 2:10–14.
  • Yao, K.N.A.I.N., T. Cohn, K. Vylomova, K. Duh, and C. Dyer (2015). Depth-gated LSTM. CoRR, abs/1508.03790 <publisher-name/>. Retrieved from http://arxiv.org/abs/1508.03790
  • Yeh, Y. -C., W. -Y. Hsiao, S. Fukayama, T. Kitahara, B. Genchel, H. -M. Liu, H. -W. Dong, Y. Chen, T. Leong, and Y. -H. Yang (2020). Automatic melody harmonization with triad chords: A comparative study. arXiv. Retrieved from https://arxiv.org/abs/2001.02360