Abstract
Music is an exciting area where the computational intelligence has cast a deep impact. In this chapter we study a few intelligent systems in the domain of music. The vast volume of music available in various formats has necessitated the need for their automated classification. Here we discuss the system for the identification of genres as well as the recognition of artists. These systems have a variety of application in playlist generation, music suggestion, music fetching etc. The other part of the chapter would focus upon the composition of music. Here also we discuss a variety of methods using Genetic Algorithms and Neural Networks. The manual assistive design of Genetic Algorithms enables the automated composition of music as per human demand. The neural approach uses a series prediction phenomenon to compose music when some part of it is known. These systems enable good composition techniques which may be employed to assist human composers in their task.
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Shukla, A., Tiwari, R., Kala, R. (2010). Intelligent Systems Design in Music. In: Towards Hybrid and Adaptive Computing. Studies in Computational Intelligence, vol 307. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14344-1_7
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DOI: https://doi.org/10.1007/978-3-642-14344-1_7
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