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      Computational Creativity and Music Generation Systems: An Introduction to the State of the Art

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          Abstract

          Computational Creativity is a multidisciplinary field that tries to obtain creative behaviors from computers. One of its most prolific subfields is that of Music Generation (also called Algorithmic Composition or Musical Metacreation), that uses computational means to compose music. Due to the multidisciplinary nature of this research field, it is sometimes hard to define precise goals and to keep track of what problems can be considered solved by state-of-the-art systems and what instead needs further developments. With this survey, we try to give a complete introduction to those who wish to explore Computational Creativity and Music Generation. To do so, we first give a picture of the research on the definition and the evaluation of creativity, both human and computational, needed to understand how computational means can be used to obtain creative behaviors and its importance within Artificial Intelligence studies. We then review the state of the art of Music Generation Systems, by citing examples for all the main approaches to music generation, and by listing the open challenges that were identified by previous reviews on the subject. For each of these challenges, we cite works that have proposed solutions, describing what still needs to be done and some possible directions for further research.

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          Most cited references195

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          Long Short-Term Memory

          Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.
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            I.—COMPUTING MACHINERY AND INTELLIGENCE

            A Turing (1950)
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              The social psychology of creativity: A componential conceptualization.

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                Author and article information

                Contributors
                Journal
                Front Artif Intell
                Front Artif Intell
                Front. Artif. Intell.
                Frontiers in Artificial Intelligence
                Frontiers Media S.A.
                2624-8212
                03 April 2020
                2020
                : 3
                : 14
                Affiliations
                Department of Information Engineering, CSC - Centro di Sonologia Computazionale, University of Padova , Padua, Italy
                Author notes

                Edited by: Roger B. Dannenberg, Carnegie Mellon University, United States

                Reviewed by: Fabio Aurelio D'Asaro, University of Naples Federico II, Italy; Rinkaj Goyal, Guru Gobind Singh Indraprastha University, India

                *Correspondence: Filippo Carnovalini filippo.carnovalini@ 123456dei.unipd.it

                This article was submitted to Machine Learning and Artificial Intelligence, a section of the journal Frontiers in Artificial Intelligence

                Article
                10.3389/frai.2020.00014
                7861321
                33733133
                81b3a335-beec-46fd-b66e-240d7e658197
                Copyright © 2020 Carnovalini and Rodà.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 30 October 2019
                : 09 March 2020
                Page count
                Figures: 0, Tables: 2, Equations: 0, References: 196, Pages: 20, Words: 20339
                Funding
                Funded by: Università degli Studi di Padova 10.13039/501100003500
                Categories
                Artificial Intelligence
                Review

                computational creativity,music generation,survey,meta-review,algorithmic composition,musical metacreation,automatic composition,computer music

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