A User Controlled System for the Generation of Melodies Applying Case Based Reasoning

Navarro-Cáceres, María; Rodrı́guez, Sara; Milla, Diego; Pérez-Lancho, Belén; Corchado, Juan M.

doi:10.1007/978-3-319-61030-6_17

Cited by 3 publications

(7 citation statements)

References 11 publications

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“…A primary objective to many music composition applications is to imitate a human composer's experience; thus, Case-Based Reasoning (CBR) makes a suitable approach for the task. María Navarro-Cáceres et al [6] combined case-based reasoning with a Markov model in a melody composition system. Their objective was to obtain the probabilities of a given note following the last note incorporated in the melody.…”

Section: Melody Pitchmentioning

confidence: 99%

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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 Pitchmentioning

confidence: 99%

“…Techniques and Applications Melody Pitch GA: 2016 [7] -CBR: 2017 [6] -DNN: 2016 [8], 2016 [9], 2017 [10] -Markov: 2017 [6] -GAN: 2017 [11], 2018 [12], 2020 [14], 2021 […”

Section: Taskmentioning

confidence: 99%

Applications of Computational Intelligence in Computer Music Composition

Siphocly

Salem

El-Horabty

2021

IJICIS

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“…The system enables a set of transformations to modify the output melody before adapting the case and storing the produced solution in the case base. Navarro-Cáceres et al [5] developed a system whose purpose is to assign probabilities for given notes following the last note of the melody. Their system was built upon a CBR architecture accompanied by a Markov model.…”

Section: Cbr In Algorithmic Compositionmentioning

confidence: 99%

“…1 labels the main musical tasks lying under the computer music composition umbrella, further with their types. Melody generation is devising the musical notes pitch; "melody" is the group of consecutive notes forming the musical piece while a note "pitch" [5] is the human interpretation of the note's frequency that distinguishes it from other notes. Timbre [6], another aspect of the musical piece's melody, is defined by the American Standard Association (ASA) to be "That attribute of sensation in terms of which a listener can judge two sounds having the same loudness and pitch are dissimilar" [7].…”

Section: Introductionmentioning

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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“…For example, Luong et al [23] apply a CBR-BDI agent as a master agent to create a role video-game. Navarro-Cáceres et al [24] develop a CBR-BDI agent capable of generating music based on the user's opinion. Yang [25] models a BDI agent to save energy applying a CBR behavior.…”

Section: A Modeling Agents With a Cbr Architecturementioning

confidence: 99%

Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings

Pinto

Faia

Navarro-Cáceres

et al. 2019

IEEE Systems Journal

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This paper proposes a novel case-based reasoning (CBR) recommender system for intelligent energy management in buildings. The proposed approach recommends the amount of energy reduction that should be applied in a building in each moment, by learning from previous similar cases. The k-nearest neighbor clustering algorithm is applied to identify the most similar past cases, and an approach based on support vector machines is used to optimize the weight of different parameters that characterize each case. An expert system composed by a set of ad hoc rules guarantees that the solution is adequate and applicable to the new case scenario. The proposed CBR methodology is modeled through a dedicated software agent, thus enabling its integration in a multi-agent systems society for the study of energy systems. Results show that the proposed approach is able to provide suitable recommendations on energy reduction, by comparing its results with a previous approach based on particle swarm optimization and with the real reduction in past cases. The applicability of the proposed approach in real scenarios is also assessed through the application of the results provided by the proposed approach on a house energy resources management system. Index Terms-Building energy management, case-based reasoning (CBR), energy efficiency, multi-agent systems (MAS).

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A User Controlled System for the Generation of Melodies Applying Case Based Reasoning

Cited by 3 publications

References 11 publications

Applications of Computational Intelligence in Computer Music Composition

Applications of Computational Intelligence in Computer Music Composition

Top 10 Artificial Intelligence Algorithms in Computer Music Composition

Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings

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