Computational Fugue Analysis

Giraud, Mathieu; Groult, Richard; Leguy, Emmanuel; Levé, Florence

doi:10.1162/comj_a_00300

Cited by 11 publications

(7 citation statements)

References 16 publications

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“…The team is composed of five MIR researchers working on music modeling (especially music analysis), with a focus on structure, harmony, and texture. Previous work of the team includes form analysis of fugues (Giraud et al, 2015) and sonata forms (Allegraud et al, 2019). Most members of the team have pursued academic studies in classical music and some have experience also in folk, choral, or electronic music.…”

Section: Team Approach and Processmentioning

confidence: 99%

I Keep Counting: An Experiment in Human/AI Co-creative Songwriting

Micchi

Bigo

Giraud

et al. 2021

Transactions of the International Society for Music Information Retrieval

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Musical co-creativity aims at making humans and computers collaborate to compose music. As an MIR team in computational musicology, we experimented with co-creativity when writing our entry to the "AI Song Contest 2020". Artificial intelligence was used to generate the song's structure, harmony, lyrics, and hook melody independently and as a basis for human composition. It was a challenge from both the creative and the technical point of view: in a very short time-frame, the team had to adapt its own simple models, or experiment with existing ones, to a related yet still unfamiliar task, music generation through AI. The song we propose is called "I Keep Counting". We openly detail the process of songwriting, arrangement, and production. This experience raised many questions on the relationship between creativity and machine, both in music analysis and generation, and on the role AI could play to assist a composer in their work. We experimented with AI as automation, mechanizing some parts of the composition, and especially AI as suggestion to foster the composer's creativity, thanks to surprising lyrics, uncommon successions of sections and unexpected chord progressions. Working with this material was thus a stimulus for human creativity.

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Section: Team Approach and Processmentioning

confidence: 99%

I Keep Counting: An Experiment in Human/AI Co-creative Songwriting

Micchi

Bigo

Giraud

et al. 2021

Transactions of the International Society for Music Information Retrieval

Self Cite

View full text Add to dashboard Cite

show abstract

“…Finally, the identification of a large-scale structure such as the sonata form requires the combination of these local features to reach a piece-level analysis, which is itself a challenge for MIR research. We previously reviewed research on computational analysis of musical form (Giraud et al, 2015). Chen et al (2004) proposed to segment the musical piece into sections called "sentences", clustering phrases predicted by the LDBM algorithm by Cambouropoulos (2001).…”

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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“…In the field of modern music acoustics, waveform charts, spectrograms, and spectrograms that expand the spectrograms over time have become the most common cases of music information visualization [6,7]. Sonic Visualizer, developed by the Digital Music Center of Queen Mary University of London, provides a platform for sound signal visualization for music researchers, archive managers, audio signal researchers and enthusiasts to conduct audio research by watching [8]. The platform, including the music technology group of the Pompeii Fabre University in Spain, can be used as a plug-in to run programs developed by various national higher education institutions and research units.…”

Section: Introductionmentioning

confidence: 99%

Research on Music Visualization Based on Graphic Images and Mathematical Statistics

2020

IEEE Access

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Music visualization can present music information through the visual way of graphic images, which is helpful to improve the accuracy and effectiveness of music information communication. In view of the shortcomings in the current music visualization field, this paper combines K-means clustering, fusion decision tree and other mathematical statistical methods on the basis of music graphic images to construct a music visualization model based on graphic images and mathematical statistics. First, the application principles of Schlieren imaging and laser Doppler imaging in the visualization of music graphic images are described. Secondly, on the basis of music graphic images, K-means clustering method is used to perform cluster analysis on music visualization information. Finally, through the fusion decision tree method, the classification of music visual information is studied. The actual case analysis and performance test results show the superiority of the music visualization method based on graphic images and mathematical statistics. This method can provide a scientific reference model and basis for the modern music industry to establish new visualization systems using graphics, images and mathematical statistics.

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Computational Fugue Analysis

Cited by 11 publications

References 16 publications

I Keep Counting: An Experiment in Human/AI Co-creative Songwriting

I Keep Counting: An Experiment in Human/AI Co-creative Songwriting

Learning Sonata Form Structure on Mozart’s String Quartets

Research on Music Visualization Based on Graphic Images and Mathematical Statistics

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