Leveraging diversity in computer-aided musical orchestration with an artificial immune system for multi-modal optimization

Caetano, Marcelo; Zacharakis, Asterios; Barbancho, Isabel; Tardón, Lorenzo J.

doi:10.1016/j.swevo.2018.12.010

Cited by 14 publications

(22 citation statements)

References 21 publications

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“…Carpentier et al [18] introduced Orchidée, a timeefficient evolutionary orchestration algorithm. Marcelo Caetano et al [19] developed an artificial immune system (AIS) called Opt-aiNet that aids in orchestration. Opt-aiNet is formulated as a multimodal system that searches for combinations of musical instrument sounds that minimize the distance to a reference sound encoded in a fitness function.…”

Section: Melody Timbrementioning

confidence: 99%

Applications of Computational Intelligence in Computer Music Composition

Siphocly

Salem

El-Horabty

2021

IJICIS

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Engaging computers in composing musical pieces is a challenging and trending field of research. The musical tasks that can be performed or aided by computers' computational powers, are numerous. This paper is concerned with applications of computational intelligence in music composition. Its main objective is to survey various computational intelligence techniques for performing miscellaneous music composition tasks. To achieve this objective, we first define each music composition task, then we discuss the recent applications of each, and the techniques adopted in them. We also highlight the most suitable techniques for performing each task. Our study shows that the most suitable techniques for human composers imitative systems are case-based reasoning and artificial neural networks. It is also shown that Markov models are more suitable for predicting musical notes based on the given previous notes. Genetic algorithms excel in chord progressions generation. Deep neural networks are clever at capturing temporal information of a musical piece. The state-of-the-art generative adversarial networks produce music as close as possible to real compositions. At the end of this study, we shed the light on many future research directions in the field of computer music composition.

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Section: Melody Timbrementioning

confidence: 99%

Applications of Computational Intelligence in Computer Music Composition

Siphocly

Salem

El-Horabty

2021

IJICIS

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“…For the task of computer aided orchestration, Caetano et al [41] developed a multi-modal AIS that comes up with new combinations of musical instrument sounds as close as possible to the encoded sound in a penalty function. In contrary to chord generation, the nature of the orchestration problem is that it may hold more than one possibility, hence the aim of Marcelo's system was to maximize the diversity in the solution set of the multimodal optimization problem.…”

Section: Ais In Algorithmic Compositionmentioning

confidence: 99%

Top 10 Artificial Intelligence Algorithms in Computer Music Composition

Siphocly¹,

El-Horbaty²,

Salem³

2021

IJCDS

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Music composition is now appealing to both musicians and non-musicians equally. It branches into various musical tasks such as the generation of melody, accompaniment, or rhythm. This paper discusses the top ten artificial intelligence algorithms with applications in computer music composition from 2010 to 2020. We give an analysis of each algorithm and highlight its recent applications in music composition tasks, shedding the light on its strengths and weaknesses. Our study gives an insight on the most suitable algorithm for each musical task, such as rule-based systems for music theory representation, case-based reasoning for capturing previous musical experiences, Markov chains for melody generation, generative grammars for fast composition of musical pieces that comply to music rules, and linear programming for timbre synthesis. Additionally, there are biologically inspired algorithms such as: genetic algorithms, and algorithms used by artificial immune systems and artificial neural networks, including shallow neural networks, deep neural networks, and generative adversarial networks. These relatively new algorithms are currently heavily used in performing numerous music composition tasks.

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“…Descriptors of timbre play a key role in several steps of recent CAMO systems [15,57,26], namely timbre description of isolated sounds, timbre description of combinations of musical instrument sounds, and timbre similarity between instrument combinations and the reference sound. The timbre of both the reference sound and of the isolated musical instrument sounds is represented with a descriptor vector comprising a subset of the traditional descriptors of timbre [14].…”

Section: Musical Orchestration With the Aid Of The Computermentioning

confidence: 99%