Tonality driven piano compositions with grammatical evolution

Loughran, Róisín; McDermott, James; O’Neill, Michael

doi:10.1109/cec.2015.7257152

Cited by 16 publications

(3 citation statements)

References 18 publications

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“…There are many examples of evolutionary algorithmic approaches to generating music, two notable examples are the methods to evolve piano pieces by Loughran et al [15] and Dahlstedt [7], although many more can be found in the Evolutionary Computer Music book [19]. Other examples of real-time music generation can be found in patents.…”

Section: Music Generation and Gamesmentioning

confidence: 99%

Evolving in-game mood-expressive music with MetaCompose

Scirea

Eklund

Togelius

et al. 2018

Proceedings of the Audio Mostly 2018 on Sound in Immersion and Emotion

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MetaCompose is a music generator based on a hybrid evolutionary technique that combines FI-2POP and multi-objective optimization. In this paper we employ the MetaCompose music generator to create music in real-time that expresses different mood-states in a game-playing environment (Checkers). In particular, this paper focuses on determining if differences in player experience can be observed when: (i) using affective-dynamic music compared to static music, and (ii) the music supports the game's internal narrative/state. Participants were tasked to play two games of Checkers while listening to two (out of three) different set-ups of game-related generated music. The possible set-ups were: static expression, consistent affective expression, and random affective expression. During game-play players wore a E4 Wristband, allowing various physiological measures to be recorded such as blood volume pulse (BVP) and electromyographic activity (EDA). The data collected confirms a hypothesis based on three out of four criteria (engagement, music quality, coherency with game excitement, and coherency with performance) that players prefer dynamic affective music when asked to reflect on the current game-state. In the future this system could allow designers/composers to easily create affective and dynamic soundtracks for interactive applications.

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Section: Music Generation and Gamesmentioning

confidence: 99%

Evolving in-game mood-expressive music with MetaCompose

Scirea

Eklund

Togelius

et al. 2018

Proceedings of the Audio Mostly 2018 on Sound in Immersion and Emotion

View full text Add to dashboard Cite

show abstract

“…They discuss ten attributes used in the evaluation of melodies based on pitch and rhythm measurements, concluding that previous approaches to formalise a fitness function for melodies have not comprehensively incorporated all measures. Nevertheless, many studies have used various types of autonomous fitness functions to drive EC systems to create music (Todd & Werner, 1999;Dahlstedt, 2007;Loughran, McDermott & O'Neill, 2015a, 2015bMunoz, Cadenas, Ong & Acampora, 2016).…”

Section: Measuring Fitnessmentioning

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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“…In the first phase, a global optimization method directs the production of artificial features from the existing ones with the help of grammatical evolution [70]. Grammatical evolution is a variation of genetic programming where the chromosomes are production rules of the target BNF grammar, and it has been used successfully in a variety of applications, such as music composition [71], economics [72], symbolic regression [73], robotics [74], and caching algorithms [75]. The global optimization method used in this work is the particle swarm optimization (PSO) method [76][77][78].…”

mentioning

confidence: 99%

A Feature Construction Method That Combines Particle Swarm Optimization and Grammatical Evolution

Tsoulos

Tzallas

2023

Applied Sciences

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The problem of data classification or data fitting is widely applicable in a multitude of scientific areas, and for this reason, a number of machine learning models have been developed. However, in many cases, these models present problems of overfitting and cannot generalize satisfactorily to unknown data. Furthermore, in many cases, many of the features of the input data do not contribute to learning, or there may even be hidden correlations between the features of the dataset. The purpose of the proposed method is to significantly reduce data classification or regression errors through the usage of a technique that utilizes the particle swarm optimization method and grammatical evolution. This method is divided into two phases. In the first phase, artificial features are constructed using grammatical evolution, and the progress of the creation of these features is controlled by the particle swarm optimization method. In addition, this new technique utilizes penalty factors to limit the generated features to a range of values to make training machine learning models more efficient. In the second phase of the proposed technique, these features are exploited to transform the original dataset, and then any machine learning method can be applied to this dataset. The performance of the proposed method was measured on some benchmark datasets from the relevant literature. Also, the method was tested against a series of widely used machine learning models. The experiments performed showed a significant improvement of 30% on average in the classification datasets and an even greater improvement of 60% in the data fitting datasets.

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Tonality driven piano compositions with grammatical evolution

Cited by 16 publications

References 18 publications

Evolving in-game mood-expressive music with MetaCompose

Evolving in-game mood-expressive music with MetaCompose

Limitations from Assumptions in Generative Music Evaluation

A Feature Construction Method That Combines Particle Swarm Optimization and Grammatical Evolution

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