MIDI2vec: Learning MIDI embeddings for reliable prediction of symbolic music metadata

Lisena, Pasquale; Meroño‐Peñuela, Albert; Troncy, Raphaël

doi:10.3233/sw-210446

Cited by 10 publications

(4 citation statements)

References 36 publications

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“…In the digitization of intangible cultural heritage, efforts tend to focus on phenomenological digitization, targeting the recording of kinetic or vocal activities [26][27][28][29][30][31]. Cinematographic and 3D motion digitization enable the recording of performing arts in multiple media and formats, exhibiting immersive qualities and interactive experiences [32,33] similar to musical content [34].…”

Section: Datamentioning

confidence: 99%

A Roadmap for Craft Understanding, Education, Training, and Preservation

Zabulis,

Partarakis,

Demeridou

et al. 2023

Heritage

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A roadmap is proposed that defines a systematic approach for craft preservation and its evaluation. The proposed roadmap aims to deepen craft understanding so that blueprints of appropriate tools that support craft documentation, education, and training can be designed while achieving preservation through the stimulation and diversification of practitioner income. In addition to this roadmap, an evaluation strategy is proposed to validate the efficacy of the developed results and provide a benchmark for the efficacy of craft preservation approaches. The proposed contribution aims at the catalyzation of craft education and training with digital aids, widening access and engagement to crafts, economizing learning, increasing exercisability, and relaxing remoteness constraints in craft learning.

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Section: Datamentioning

confidence: 99%

A Roadmap for Craft Understanding, Education, Training, and Preservation

Zabulis,

Partarakis,

Demeridou

et al. 2023

Heritage

View full text Add to dashboard Cite

show abstract

Section: Datamentioning

confidence: 99%

A Roadmap for Craft Understanding, Education, Training, and Preservation

Zabulis,

Partarakis,

Demeridou

et al. 2023

Preprint

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A roadmap is proposed that defines a systematic approach for craft preservation and its evaluation. The proposed roadmap aims at deepening craft understanding, so blueprints of appropriate tools that support craft documentation, education, and training can be designed while achieving preservation through the stimulation and diversification of practitioner income. A roadmap is required because understanding making activities that include “care, judgement, and dexterity” [1] call for interdisciplinary contributions from Anthropology, Cognitive Science, Art History, as well as Physical and Computational Sciences, to cover the multifaceted expression of crafts as living and developing heritage, as a source of income, and as the expression of the mind through “imagery, technology, and sedimented knowledge” [2]. In addition to this roadmap, an evaluation strategy is proposed to validate the efficacy of the developed results and provide a benchmark for the efficacy of craft preservation approaches. The proposed contribution aims at the catalysation of craft education and training with digital aids, widening access and engagement to crafts, economising learning, increasing exercisability, and relaxing remoteness constraints in craft learning.

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“…For the same task, in Bernardo & Langlois (2008) authors used the pitch levels and durations that describe each track to extract a set of features that are used to train a classifier. Recently, Lisena, Meroño-Peñuela & Troncy (2022) used graph embedding techniques to represent MIDI files as vectors. Basically, MIDI files were represented as a graph and node2vec ( Grover & Leskovec, 2016 ) was then run to generate embeddings using random walks in the graph.…”

Section: Related Workmentioning

confidence: 99%

A transformers-based approach for fine and coarse-grained classification and generation of MIDI songs and soundtracks

Angioni

Lincoln-DeCusatis

Ibba

et al. 2023

PeerJ Computer Science

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Music is an extremely subjective art form whose commodification via the recording industry in the 20th century has led to an increasingly subdivided set of genre labels that attempt to organize musical styles into definite categories. Music psychology has been studying the processes through which music is perceived, created, responded to, and incorporated into everyday life, and, modern artificial intelligence technology can be exploited in such a direction. Music classification and generation are emerging fields that gained much attention recently, especially with the latest discoveries within deep learning technologies. Self attention networks have in fact brought huge benefits for several tasks of classification and generation in different domains where data of different types were used (text, images, videos, sounds). In this article, we want to analyze the effectiveness of Transformers for both classification and generation tasks and study the performances of classification at different granularity and of generation using different human and automatic metrics. The input data consist of MIDI sounds that we have considered from different datasets: sounds from 397 Nintendo Entertainment System video games, classical pieces, and rock songs from different composers and bands. We have performed classification tasks within each dataset to identify the types or composers of each sample (fine-grained) and classification at a higher level. In the latter, we combined the three datasets together with the goal of identifying for each sample just NES, rock, or classical (coarse-grained) pieces. The proposed transformers-based approach outperformed competitors based on deep learning and machine learning approaches. Finally, the generation task has been carried out on each dataset and the resulting samples have been evaluated using human and automatic metrics (the local alignment).

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MIDI2vec: Learning MIDI embeddings for reliable prediction of symbolic music metadata

Cited by 10 publications

References 36 publications

A Roadmap for Craft Understanding, Education, Training, and Preservation

A Roadmap for Craft Understanding, Education, Training, and Preservation

A Roadmap for Craft Understanding, Education, Training, and Preservation

A transformers-based approach for fine and coarse-grained classification and generation of MIDI songs and soundtracks

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