Pachet, F., Roy, P.: Markov constraints: steerable generation of markov sequences. In: Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR 2017), Suzhou, China, pp. ![]() ![]() Pachet, F., Papadopoulos, A., Roy, P.: Sampling variations of sequences for structured music generation. Monteith, K., Martinez, T.R., Ventura, D.: Automatic generation of melodic accompaniments for lyrics. Lafferty, J., McCallum, A., Pereira, F.C.: Conditional random fields: probabilistic models for segmenting and labeling sequence data (2001) In: Proceedings 7th Sound and Music Computing Conference (SMC), pp. In: Proceedings of the International Conference on Learning Representations (ICLR) (2015)įukayama, S., Nakatsuma, K., Sako, S., Nishimoto, T., Sagayama, S.: Automatic song composition from the lyrics exploiting prosody of the Japanese language. Kingma, D.P., Jimmy, B.: Adam: a method for stochastic optimization. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, Doha, Qatar, 25–29 October 2014, A meeting of SIGDAT, a Special Interest Group of the ACL, pp. In: Proceedings of the 9th International Conference on Music Perception and Cognition, ICMPC (2006)Ĭho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. Ĭhan, M., Potter, J., Schubert, E.: Improving algorithmic music composition with machine learning. īahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: Correia, J., Ciesielski, V., Liapis, A. KeywordsĪckerman, M., Loker, D.: Algorithmic songwriting with ALYSIA. Results indicate that our generated melodies are more melodious and tuneful compared with the baseline method. In addition, we apply a singing voice synthesizer software to synthesize the “singing” of the lyrics and melodies for human evaluation. Experimental results on lyrics-melody pairs of 18,451 pop songs demonstrate the effectiveness of our proposed methods. It consists of two neural encoders to encode the current lyrics and the context melody respectively, and a hierarchical decoder to jointly produce musical notes and the corresponding alignment. More specifically, we develop the melody composition model based on the sequence-to-sequence framework. Given the lyrics as input, we propose a melody composition model that generates lyrics-conditional melody as well as the exact alignment between the generated melody and the given lyrics simultaneously. In this paper, we study a novel task that learns to compose music from natural language.
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