AI Methods in Algorithmic Composition: A Comprehensive Survey

Journal of Artificial Intelligence Research

Algorithmic composition is the partial or total automation of the process of music composition by using computers. Since the 1950s, different computational techniques related to Artificial Intelligence have been used for algorithmic composition, including grammatical representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint programming and evolutionary algorithms. This survey aims to be a comprehensive account of research on algorithmic composition, presenting a thorough view of the field for researchers in Artificial Intelligence.


Worldwide AI

AI Magazine

Neuromorphic, evolutionary, or fuzzylike systems have been developed by many research groups in the Spanish computer sciences. It is no surprise, then, that these naturegrounded efforts start to emerge, enriching the AI catalogue of research projects and publications and, eventually, leading to new directions of basic or applied research. In this article, we review the contribution of Melomics in computational creativity. In Spain there are 74 universities, many of which have computer science departments that host AIrelated research groups. AEPIA, the Spanish society for AI research, was founded in 1983 and has been vigorously promoting the advancement of AI since then. Along with several other societies and communities of interest, it promotes various periodic conferences and workshops. The Artificial Intelligence Research Institute (IIIA) of the Spanish National Research Council constitutes one of the flagships of local AI research. Ramón López de Mántaras, IIIA's renowned director, was one of the pioneers of AI in Spain, and he also was the recipient of the prestigious AAAI Englemore Award in 2011. Other researchers that have reached an outstanding position, and lead important research groups in Spain, include Antonio Bahamonde (University of Oviedo), Federico Barber (Polytechnic University of Madrid), Vicent Botti (Polytechnic University of Valencia), and Amparo Vila (University of Granada). This department, with more than one hundred faculty members, is organized in several research groups, three of which maintain active AI research lines. Melomics is a new approach in artificial creativity (for a perspective on this discipline, see the 2009 fall issue of AI Magazine). More specifically, it focuses on algorithmic composition and aims at the full automation of the composition process of professional music.


Melomics: A Case-Study of AI in Spain

AI Magazine

Traditionally focused on good old-fashioned AI and robotics, the Spanish AI community holds a vigorous computational intelligence substrate. Neuromorphic, evolutionary, or fuzzylike systems have been developed by many research groups in the Spanish computer sciences. It is no surprise, then, that these naturegrounded efforts start to emerge, enriching the AI catalogue of research projects and publications and, eventually, leading to new directions of basic or applied research. In this article, we review the contribution of Melomics in computational creativity.


Genetic Hierarchical Music Structures

AAAI Conferences

Music has structure at many levels, from grand arrangements of verses and choruses down to patterns in small riffs and themes. A bracketed L-system -- the SARAH language -- is used to represent compositions in terms of these hierarchical structures, allowing genetic programming to meaningfully mutate and crossbred compositions at all levels of structure. Automated composition becomes possible, using evolution with human aesthetic judgment as a fitness function. SARAH is also human-readable and can be used as a human tool for rapid structural composition development; or as a semi-automated composition system, mixing human and evolved contributions. The system has bred pleasing compositions starting from basic musical materials and has also been used to crossbreed Bach with the Spice Girls: these examples are presented as audio files.


Embracing the Bias of the Machine: Exploring Non-Human Fitness Functions

AAAI Conferences

Autonomous aesthetic evaluation is the Holy Grail of generative music, and one of the great challenges of computational creativity. Unlike most other computational activities, there is no notion of optimality in evaluating creative output: there are subjective impressions involved, and framing obviously plays a big role. When developing metacreative systems, a purely objective fitness function is not available: the designer is thus faced with how much of their own aesthetic to include. Can a generative system be free of the designer’s bias? This paper presents a system that incorporates an aesthetic selection process that allows for both human-designed and non-human fitness functions.