Abstract
Music generation with the aid of computers has been recently grabbed the attention of many scientists in the area of artificial intelligence. Deep learning techniques have evolved sequence production methods for this purpose. Yet, a challenging problem is how to evaluate a music generated by a machine. In this paper, a methodology has been developed based upon an interactive evolutionary optimization method, with which the scoring of the generated musics are primarily performed by human expertise, during the training. This music quality scoring is modeled using a BiLSTM recurrent neural network. Moreover, the innovative generated music through a Genetic algorithm, will then be evaluated using this BiLSTM network. The results of this mechanism clearly show that the proposed method is able to create pleasurable melodies with desired styles and pieces. This method is also quite fast, compared to the state-of-the-art data-oriented evolutionary systems.
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References
Agarwal, S., Saxena, V., Singal, V., Aggarwal, S.: LSTM based music generation with dataset preprocessing and reconstruction techniques. In: 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 455–462. IEEE (2018)
Agres, K., Herremans, D., Bigo, L., Conklin, D.: Harmonic structure predicts the enjoyment of uplifting trance music. Front. Psychol. 7, 1999 (2017)
Agres, K.R., DeLong, J.E., Spivey, M.: The sparsity of simple recurrent networks in musical structure learning. In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 31 (2009)
Boulanger-Lewandowski, N., Bengio, Y., Vincent, P.: Modeling temporal dependencies in high-dimensional sequences: application to polyphonic music generation and transcription. arXiv preprint arXiv:1206.6392 (2012)
Brooks, F.P., Hopkins, A., Neumann, P.G., Wright, W.V.: An experiment in musical composition. IRE Trans. Electron. Comput. 3, 175–182 (1957)
Browne, T.M., Fox, C.: Global expectation-violation as fitness function in evolutionary composition. In: Giacobini, M., et al. (eds.) EvoWorkshops 2009. LNCS, vol. 5484, pp. 538–546. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01129-0_60
Davismoon, S., Eccles, J.: Combining musical constraints with Markov transition probabilities to improve the generation of creative musical structures. In: Di Chio, C., et al. (eds.) EvoApplications 2010. LNCS, vol. 6025, pp. 361–370. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12242-2_37
Eck, D., Schmidhuber, J.: A first look at music composition using LSTM recurrent neural networks. Istituto Dalle Molle Di Studi Sull IntelligenzaArtificiale 103, 48 (2002)
Herremans, D.: Morpheus: automatic music generation with recurrent pattern constraints and tension profiles (2016)
Herremans, D., Chuan, C.H.: Modeling musical context with word2vec. arXiv preprint arXiv:1706.09088 (2017)
Herremans, D., Sörensen, K.: Composing first species counterpoint with a variable neighbourhood search algorithm. J. Math. Arts 6(4), 169–189 (2012)
Herremans, D., Sörensen, K.: Composing fifth species counterpoint music with a variable neighborhood search algorithm. Expert Syst. Appl. 40(16), 6427–6437 (2013)
Hofmann, D.M.: A genetic programming approach to generating musical compositions. In: Johnson, C., Carballal, A., Correia, J. (eds.) EvoMUSART 2015. LNCS, vol. 9027, pp. 89–100. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16498-4_9
Horner, A., Goldberg, D.E.: Genetic algorithms and computer-assisted music composition. In: ICMC 1991, pp. 479–482 (1991)
Kaliakatsos-Papakostas, M.A., Floros, A., Vrahatis, M.N.: Interactive music composition driven by feature evolution. SpringerPlus 5(1), 826 (2016)
Ponce de León, P.J., Iñesta, J.M., Calvo-Zaragoza, J., Rizo, D.: Data-based melody generation through multi-objective evolutionary computation. J. Math. Music 10(2), 173–192 (2016)
Loughran, R., McDermott, J., O’Neill, M.: Grammatical music composition with dissimilarity driven hill climbing. In: Johnson, C., Ciesielski, V., Correia, J., Machado, P. (eds.) EvoMUSART 2016. LNCS, vol. 9596, pp. 110–125. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31008-4_8
Makris, D., Kaliakatsos-Papakostas, M., Karydis, I., Kermanidis, K.L.: Combining LSTM and feed forward neural networks for conditional rhythm composition. In: Boracchi, G., Iliadis, L., Jayne, C., Likas, A. (eds.) EANN 2017. CCIS, vol. 744, pp. 570–582. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65172-9_48
Manzelli, R., Thakkar, V., Siahkamari, A., Kulis, B.: An end to end model for automatic music generation: combining deep raw and symbolic audio networks. In: Proceedings of the Musical Metacreation Workshop at 9th International Conference on Computational Creativity, Salamanca, Spain (2018)
McVicar, M., Fukayama, S., Goto, M.: AutoLeadGuitar: automatic generation of guitar solo phrases in the tablature space. In: 2014 12th International Conference on Signal Processing (ICSP), pp. 599–604. IEEE (2014)
Mishra, A., Tripathi, K., Gupta, L., Singh, K.P.: Long short-term memory recurrent neural network architectures for melody generation. In: Bansal, J.C., Das, K.N., Nagar, A., Deep, K., Ojha, A.K. (eds.) Soft Computing for Problem Solving. AISC, vol. 817, pp. 41–55. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-1595-4_4
Lewis, J.P.: Creation by refinement and the problem of algorithmic music composition. In: Todd, P.M., Loy, G. (eds.) Music and Connectionism, p. 212. MIT Press, Cambridge (1991)
Pachet, F., Roy, P., Barbieri, G.: Finite-length Markov processes with constraints. In: Twenty-Second International Joint Conference on Artificial Intelligence (2011)
Papadopoulos, A., Roy, P., Pachet, F.: Avoiding plagiarism in Markov sequence generation. In: Twenty-Eighth AAAI Conference on Artificial Intelligence (2014)
Pinkerton, R.C.: Information theory and melody. Sci. Am. 194(2), 77–87 (1956)
Scirea, M., Togelius, J., Eklund, P., Risi, S.: Affective evolutionary music composition with metacompose. Genet. Program. Evolvable Mach. 18(4), 433–465 (2017)
Todd, P.M.: A connectionist approach to algorithmic composition. Comput. Music J. 13(4), 27–43 (1989)
Tokui, N., Iba, H., et al.: Music composition with interactive evolutionary computation. In: Proceedings of the Third International Conference on Generative Art, vol. 17, pp. 215–226 (2000)
Tuohy, D.R., Potter, W.D.: A genetic algorithm for the automatic generation of playable guitar tablature. In: ICMC, pp. 499–502 (2005)
Waschka II, R.: Composing with genetic algorithms: GenDash. In: Miranda, E.R., Biles, J.A. (eds.) Evolutionary Computer Music, pp. 117–136. Springer, London (2007). https://doi.org/10.1007/978-1-84628-600-1_6
Wu, J., Hu, C., Wang, Y., Hu, X., Zhu, J.: A hierarchical recurrent neural network for symbolic melody generation. arXiv preprint arXiv:1712.05274 (2017)
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Farzaneh, M., Mahdian Toroghi, R. (2020). Music Generation Using an Interactive Evolutionary Algorithm. In: Djeddi, C., Jamil, A., Siddiqi, I. (eds) Pattern Recognition and Artificial Intelligence. MedPRAI 2019. Communications in Computer and Information Science, vol 1144. Springer, Cham. https://doi.org/10.1007/978-3-030-37548-5_16
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