Este episodio comenta aplicaciones de la inteligencia artificial (en particular de aprendizaje de máquina) dentro de la música. Referencias utilizadas dentro de este episodio: Rodgers, W., Yeung, F., Odindo, C., & Degbey, W. Y. (2021). Artificial intelligence-driven music biometrics influencing customers’ retail buying behavior. Journal of Business Research, 126, 401-414. Sturm, B. L., Ben-Tal, O., Monaghan, Ú., Collins, N., Herremans, D., Chew, E., ... & Pachet, F. (2019). Machine learning research that matters for music creation: A case study. Journal of New Music Research, 48(1), 36-55. Garg, A., Chaturvedi, V., Kaur, A. B., Varshney, V., & Parashar, A. (2022). Machine learning model for mapping of music mood and human emotion based on physiological signals. Multimedia Tools and Applications, 1-41. Folk-rnn is a project funded by the UK Arts and Humanities Research Council, grant no. AH/R004706/1: "Engaging three user communities with applications and outcomes of computational music creativity". The generated tunes and the patterns of use that generated them may be used for research purposes, such as this grant. The original folk-rnn project page, where the algorithm and models were developed, is here: https://github.com/IraKorshunova/folk-rnn. It links to several compositions created by folk-rnn that have been performed live, analysed and so on. #Podcast #InteligenciaArtificial #MachineLearning #Ciencia #Tecnología #AprendizajeDeMaquina #AprendizajeAutomatico

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