ImproteK

Nika, Jérôme; Chemillier, Marc; Assayag, Gérard

doi:10.1145/3022635

Cited by 15 publications

(3 citation statements)

References 26 publications

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“…Deeper descriptions of these agents and our previous performance practice can be found in our 2023 NIME paper (Davis et al 2023, 3). Updated versions of the DYCI2 and Spire Muse software are named dicy2 and Co-Creative Communication Platform (CCCP) respectively, although we do not address the most recent updates below (Nika et al 2022; N. J. Thelle and Waerstad 2023).…”

Section: The Etu{db}e Research-creation Frameworkmentioning

confidence: 99%

eTu{d,b}e: DEVELOPING AND PERFORMING SPATIALIZATION MODELS FOR IMPROVISING MUSICAL AGENTS

Pocius,

Davis,

Cusson

2024

Proceedings of International Conference on Arts and Humanities

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The eTube is a simple acoustic instrument outfitted with a microphone and a two-button controller which we program to facilitate interaction between an improviser and improvising musical agents. Through an iterative research-creation process, we develop and perform various etudes with the eTube and musical agents, generating new knowledge through musical creation. Through this process, we have developed two interactive spatialization systems for the eTube.We begin by describing the eTu{d,b}e framework which refers to the eTube instrument and a series of improvised etudes based on human-computer musical interactions. An overview of the instrument is presented, as well as the existing systems as they were when Pocius began the spatialization portion of this project. Secondly, we outline two interactive spatialization systems designed for improvised performance with the eTube and musical agents. An overview of the updates made to the eTu{d,b}e framework is presented, followed by a description of the two spatialization systems. Finally, six different performances are presented as case studies to emphasize the advancements and challenges of the project as new approaches are developed.

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Section: The Etu{db}e Research-creation Frameworkmentioning

confidence: 99%

eTu{d,b}e: DEVELOPING AND PERFORMING SPATIALIZATION MODELS FOR IMPROVISING MUSICAL AGENTS

Pocius,

Davis,

Cusson

2024

Proceedings of International Conference on Arts and Humanities

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“…Herremans and Chew (2017) proposed to formulate this task as a combinatorial optimization problem. Nika et al (2016) used harmonic scenarios to produce structured music improvisation. Medeot et al (2018) elaborated a Recurrent Neural Network trained on a dataset of structural elements.…”

Section: Mir High-level Structure and Sonata Formmentioning

confidence: 99%

Learning Sonata Form Structure on Mozart’s String Quartets

Allegraud¹,

Bigo²,

Feisthauer³

et al. 2019

Transactions of the International Society for Music Information Retrieval

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The musical analysis of large-scale structures, such as the classical sonata form, requires to integrate multiple analyses of local musical events into a global coherent analysis. Modelling large-scale structures is still a challenging task for the research community. It includes building large and accurate annotated corpora, as well as developing practical and efficient tools in order to visualize the analyses of these corpora. It finally requires the conception of effective and properly evaluated MIR algorithms. We propose a machine learning approach for the sonata form structure on 32 movements from Mozart's string quartets. We release an open dataset, encoding two reference analyses of these 32 movements, totaling more than 1800 curated annotations, as well as flexible visualizations of these analyses. We discuss the occurrence in this corpus of melodic, harmonic, and rhythmic features induced by pitches, durations, and rests. We investigate whether the presence or the absence of these features can be characteristic of the different sections forming a sonata form. We then compute the emission and transition probabilities of several Hidden Markov Models intended to match the structure of sonata forms at several resolutions. Our results confirm that the sonata form is better identified when the parameters are learned rather than manually set up. These results open perspectives on the computational analysis of musical forms by mixing human knowledge and machine learning from annotated scores.

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“…The literature [9] introduces a guided music generation model and a responsive architecture, focusing on the control, production, and composition of human-computer music improvisation. The problem of the dialectic between reactivity and planning in interactive music improvisation is addressed through the integration of temporal norms in the music generation process through fixed or dynamic "scenarios".…”

Section: Introductionmentioning

confidence: 99%

Application of genetic algorithm in model music composition innovation

Xu,

2023

Applied Mathematics and Nonlinear Sciences

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The genetic algorithm-assisted musical composition combines evolutionary computational techniques and artistic composition. In this article, the evolution of musical compositions based on traditional genetic algorithm compositions is combined with interactive genetic algorithm techniques, mainly in model music innovation, using a genetic algorithm to generate an initial population with real number encoding of all individuals. The article focuses on studying the fitness function used to automatically evaluate the automatically generated melodies, perform evolutionary operations on the generated melodies, and generate high-quality model music melodies based on the treatment of evolutionary operations. The evaluation time of the generated melodies by the improved model is reduced from 4 minutes to 0.002 seconds, which makes the composer much more efficient in performing model music composition. The evaluation data of 16 pieces before and after the genetic manipulation were also compared, and it was found that the system evaluated the music after the genetic manipulation significantly higher than the music without the genetic manipulation. The different forms of musical compositions created by the composing system developed based on the technical research of computer genetic algorithm-assisted model music innovation are also a useful supplement to composers’ creations and open a new chapter for the field of artificial intelligence.

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ImproteK

Cited by 15 publications

References 26 publications

eTu{d,b}e: DEVELOPING AND PERFORMING SPATIALIZATION MODELS FOR IMPROVISING MUSICAL AGENTS

eTu{d,b}e: DEVELOPING AND PERFORMING SPATIALIZATION MODELS FOR IMPROVISING MUSICAL AGENTS

Learning Sonata Form Structure on Mozart’s String Quartets

Application of genetic algorithm in model music composition innovation

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