On the impact of domain-specific knowledge in evolutionary music composition

Sulyok, Csaba; Harte, Christopher; Bodó, Zalán

doi:10.1145/3321707.3321710

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…Although its target is not only genetic algorithms but a variety of machine learning techniques, our framework uses the evolutionary analogy, similarly to many other automatic composition systems [22,23]. The Darwinian metaphor can be confusing, since each system has its particular application of the same terms, sometimes denoting even opposite concepts.…”

Section: Definitions and Analogiesmentioning

confidence: 99%

GenoMus: Representing Procedural Musical Structures with an Encoded Functional Grammar Optimized for Metaprogramming and Machine Learning

2022

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We present GenoMus, a new model for artificial musical creativity based on a procedural approach, able to represent compositional techniques behind a musical score. This model aims to build a framework for automatic creativity, that is easily adaptable to other domains beyond music. The core of GenoMus is a functional grammar designed to cover a wide range of styles, integrating traditional and contemporary composing techniques. In its encoded form, both composing methods and music scores are represented as one-dimensional arrays of normalized values. On the other hand, the decoded form of GenoMus grammar is human-readable, allowing for manual editing and the implementation of user-defined processes. Musical procedures (genotypes) are functional trees, able to generate musical scores (phenotypes). Each subprocess uses the same generic functional structure, regardless of the time scale, polyphonic structure, or traditional or algorithmic process being employed. Some works produced with the algorithm have been already published. This highly homogeneous and modular approach simplifies metaprogramming and maximizes search space. Its abstract and compact representation of musical knowledge as pure numeric arrays is optimized for the application of different machine learning paradigms.

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Section: Definitions and Analogiesmentioning

confidence: 99%

GenoMus: Representing Procedural Musical Structures with an Encoded Functional Grammar Optimized for Metaprogramming and Machine Learning

2022

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“…However, a common technique to improve an optimization algorithm is to use domain-specific knowledge. 12 In this paper, we explore another method of improving the optimization algorithm via data-mining techniques, which may lead to a better understanding of the domain-specific knowledge that is not obvious to a human given the complexity of the problem.…”

Section: Introductionmentioning

confidence: 99%

Using data-mining techniques to improve combinatorial optimization algorithms

Jamieson

Gharibian

Shannon³

et al. 2022

Journal of Algorithms & Computational Technology

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In this work, we show how data-mining can be used to cluster algorithmic generated data and use that data to improve algorithms that solve combinatorial optimization problems for a real-world application—the field-programmable gate array placement problem. Our methodology is a means for other algorithm engineers to improve their own algorithms for specific real-world problems that are hard to improve. In our case, the placement algorithms are difficult to improve, and to find better heuristics we analyze the results of placement solutions to find clustered information which can then be used to improve the algorithms. Specifically, we show a technique for gathering cluster information about placement, we create a new simulated annealing algorithm and a new genetic algorithm that can deal with a mixed granularity of placement objects on a virtual field-programmable gate array, and we show that these algorithms either execute faster or improve the overall quality of solution compared to their basic algorithm without this clustering data and improved heuristics. For our improved simulated annealing placer we improve the algorithms run-time by 17% across a range of benchmarks, and our genetic algorithm improves placement metrics—-critical path by 10% and channel-width by 4%.

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Artificial intelligence in music: recent trends and challenges

Mycka,

Mańdziuk

2024

Neural Comput & Applic

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Music has always been an essential aspect of human culture, and the methods for its creation and analysis have evolved alongside the advancement of computational capabilities. With the emergence of artificial intelligence (AI) and one of its major goals referring to mimicking human creativity, the interest in music-related research has increased significantly. This review examines current literature from renowned journals and top-tier conferences, published between 2017 and 2023, regarding the application of AI to music-related topics. The study proposes a division of AI-in-music research into three major categories: music classification, music generation and music recommendation. Each category is segmented into smaller thematic areas, with detailed analysis of their inter- and intra-similarities and differences. The second part of the study is devoted to the presentation of the AI methods employed, with specific attention given to deep neural networks—the prevailing approach in this domain, nowadays. In addition, real-life applications and copyright aspects of generated music are outlined. We believe that a detailed presentation of the field along with pointing out possible future challenges in the area will be of some value for both the established AI-in-music researchers, as well as the new scholars entering this fascinating field.

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On the impact of domain-specific knowledge in evolutionary music composition

Cited by 4 publications

References 26 publications

GenoMus: Representing Procedural Musical Structures with an Encoded Functional Grammar Optimized for Metaprogramming and Machine Learning

GenoMus: Representing Procedural Musical Structures with an Encoded Functional Grammar Optimized for Metaprogramming and Machine Learning

Using data-mining techniques to improve combinatorial optimization algorithms

Artificial intelligence in music: recent trends and challenges

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