On the Documentation of Electronic Music

Lemouton, Serge; Bonardi, Alain; Pottier, Laurent; Warnier, Jacques

doi:10.1162/comj_a_00486

Cited by 10 publications

(7 citation statements)

References 10 publications

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“…Music is an art that can effectively show human emotions. At the same time, music is a note composed of a specific rhythm, melody, or musical instrument according to certain rules [1][2][3]. Rock music, jazz, classical music, and other music genres are examples of diverse style tracks comprised of unique beats, timbres, and other aspects exhibited in music works.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation

Pan

2022

Journal of Mathematics

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Aiming at the problems of poor classification effect, low accuracy, and long time in the current automatic classification methods of music genres, an automatic classification method of music genres based on deep belief network and sparse representation is proposed. The music signal is preprocessed by framing, pre-emphasis, and windowing, and the characteristic parameters of the music signal are extracted by Mel frequency cepstrum coefficient analysis. The restricted Boltzmann machine is trained layer by layer to obtain the connection weights between layers of the depth belief network model. According to the output classification, the connection weights in the model are fine-tuned by using the error back-propagation algorithm. Based on the deep belief network model after fine-tuning training, the structure of the music genre classification network model is designed. Combined with the classification algorithm of sparse representation, for the training samples of sparse representation music genre, the sparse solution is obtained by using the minimum norm, the sparse representation of test vector is calculated, the category of training samples is judged, and the automatic classification of music genre is realized. The experimental results show that the music genre automatic classification effect of the proposed method is better, the classification accuracy rate is higher, and the classification time can be effectively shortened.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation

Pan

2022

Journal of Mathematics

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“…This documentation should be sustainable, because we have seen that for data preservation over time paper always seems to be a better solution than digital archives. The latter can not always guarantee physical integrity or content intelligibility [11].…”

Section: 22mentioning

confidence: 99%

“…Notating the piece's electronic side (i.e., performance logic) before the premiere is not an absolute prerequisite for playing the piece, and usually, interactive music composers move on to a new project after the premiere. Consequently, the information for reperforming the piece is usually missing, or at best incomplete [11]. However, there are notable exceptions, such as Stockhausen's works [8].…”

Section: Passive Role Of Music Representation During Performancementioning

confidence: 99%

“…Both parts are played on the same digital piano, achieving an augmented version of the human performance. Some qualitative insights were obtained from two interviews conducted with two professional musicians well-versed with live-electronics music: the pianist Lisa Leong 10 and the composer Jorge Variego 11 . During the interviews, the scores were discussed where only a brief introduction about the proposed syntax was given, and each interviewee read the scores individually before the discussion.…”

Section: Composition and Performancementioning

confidence: 99%

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Extending Music Notation as a Programming Language for Interactive Music

Martı́nez

2021

ACM International Conference on Interactive Media Experiences

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This work describes a novel approach for composing and performing interactive music by extending the traditional staf notation to the programming language domain. The proposed syntax aims to describe the interaction between humans and computers in liveelectronics music performance. Thus, both performers and machines will understand this new notation, creating a cohesive music representation for performance that is both human-readable and technology-independent. This paper starts by describing some critical issues related to live-electronics that make it challenging to build repertoire around this genre. Next, the proposed approach is detailed, along with some syntax examples. Finally, the last section describes the evaluation of the proposed approach, including a description of the software implementation and a set of short interactive-pieces.

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“…Developed as an outgrowth of IRCAM's participation in the European CASPAR project, the Sidney server was spearheaded by Alain Bonardi, Serge Lemouton, and others as a database intended to provide "technical information necessary to play the pieces composed at IRCAM," where one can find "required equipment, technical diagrams, set-up instructions, and more" (http://brahms.ircam.fr/sidney/). For further information, see Barthélemy 2008 andLemouton et al 2018 Return to text 9. The rate at which parametric values were updated during interpolative processes (i.e., the "quantum rate" in Example 6) was hitched to the repetition of the excitation source which, in early versions of CHANT, equated with a voice's fundamental frequency.…”

Section: Transduction As An Analytical Frame For Archival Music Researchmentioning

confidence: 99%

Encoding Post-Spectral Sound

Morrison

2021

MTO

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This article examines computer-based music (ca. 1982–87) created by Finnish composer Kaija Saariaho at the Institut de Recherche et Coordination Acoustique/Musique (IRCAM) in Paris. A detailed account of archival materials for an early étude in voice synthesis, Vers le blanc (1982), demonstrates the music-theoretical import of software to Saariaho’s development of a robust compositional method that resonated with the emergent aesthetics of a post-spectral milieu. Subsequent analyses of two additional works from this period—Jardin secret II (1984–86) for harpsichord and tape, and IO (1987) for large ensemble and electronics—serve to illustrate Saariaho’s extension of this method into instrumental settings. Specific techniques highlighted include the use of interpolation systems to create continuous processes of transformation, the organization of individual musical parameters into multidimensional formal networks, and the exploration of harmonic structures based on the analysis of timbral phenomena. Relating these techniques to the affordances of contemporaneous IRCAM technologies, including CHANT, FORMES, and Saariaho’s own customized program, “transkaija,” this article adopts a transductive approach to archival research that is responsive to the diverse media artifacts associated with computer-based composition.

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On the Documentation of Electronic Music

Cited by 10 publications

References 10 publications

[Retracted] Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation

[Retracted] Automatic Classification Method of Music Genres Based on Deep Belief Network and Sparse Representation

Extending Music Notation as a Programming Language for Interactive Music

Encoding Post-Spectral Sound

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