Vox Populi: An Interactive Evolutionary System for Algorithmic Music Composition

Moroni, Artemis; Manzolli, Jônatas; Zuben, Fernando J. Von; Gudwin, Ricardo

doi:10.1162/096112100570602

Cited by 76 publications

(58 citation statements)

References 9 publications

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“…The system has been modified and developed into a real-time, MIDIbased, interactive improvisation and performance system that regularly performs in mainstream venues (Biles, 2013). VoxPopuli is an interactive compositional tool that uses evolutionary methods in real-time algorithmic music composition using notes and chords (Moroni, Manzolli, Von Zuben & Gudwin, 2000). Dahlstedt has discussed how we may use EC as the basis of a wide range of tools but that in doing so we may have to relinquish some level of control (2007,2009).…”

Section: Evolutionary Compositionmentioning

confidence: 99%

Limitations from Assumptions in Generative Music Evaluation

O’Neill

Loughran

2017

Journal of Creative Music Systems

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Abstract. The merit of a given piece of music is difficult to evaluate objectively; the merit of a computational system that creates such a piece of music may be even more so. In this article, we propose that there may be limitations resulting from assumptions made in the evaluation of autonomous compositional or creative systems. The article offers a review of computational creativity, evolutionary compositional methods and current methods of evaluating creativity. We propose that there are potential limitations in the discussion and evaluation of generative systems from two standpoints. First, many systems only consider evaluating the final artefact produced by the system whereas computational creativity is defined as a behaviour exhibited by a system. Second, artefacts tend to be evaluated according to recognised human standards. We propose that while this may be a natural assumption, this focus on human-like or human-based preferences could be limiting the potential and generality of future music generating or creative-AI systems.

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Section: Evolutionary Compositionmentioning

confidence: 99%

Limitations from Assumptions in Generative Music Evaluation

O’Neill

Loughran

2017

Journal of Creative Music Systems

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“…Some works (Wiggins et al 1998) use music-theoretical knowledge as a fitness function, like Moroni et al (1994), who use a fitness criterion that takes into account melodic fitness, harmonic fitness, and voice range fitness. Other previous works try to induce musical structure from a corpus to get new melodies (Cope 1991).…”

Section: Autonomous Fitness Functionsmentioning

confidence: 99%

Data-based melody generation through multi-objective evolutionary computation

León

Iñesta

Calvo-Zaragoza

et al. 2016

Journal of Mathematics and Music

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Genetic-based composition algorithms are able to explore an immense space of possibilities, but the main difficulty has always been the implementation of the selection process. In this work, sets of melodies are utilized for training a machine learning approach to compute fitness, based on different metrics. The fitness of a candidate is provided by combining the metrics, but their values can range through different orders of magnitude and evolve in different ways, which makes it hard to combine these criteria. In order to solve this problem, a multi-objective fitness approach is proposed, in which the best individuals are those in the Pareto-optimal frontier of the multi-dimensional fitness space. Melodic trees are also proposed as a data structure for chromosomic representation of melodies and genetic operators are adapted to them. Some experiments have been carried out using a graphical interface prototype that allows one to explore the creative capabilities of the proposed system. An Online Supplement is provided where the reader can find some technical details, information about the data used, generated melodies, and additional information about the developed prototype and its performance.

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“…Again, user rating was fed into an ANN so that low-rated individuals could be acknowledged and discarded before the user had a chance to hear them. More recent approaches incorporate the evolution of several musical aspects (such as rhythm, tonality, and style) based on separate human ratings for each aspect (Fortier and Van Dyne, 2011;Moroni et al, 2000).…”

Section: Interactive Composition With Ga and Gpmentioning

confidence: 99%