Activities per year
Abstract
The concept of a Binary Multi-track Sequential Generative Adversarial Network (BinaryMuseGAN) used for the generation of music has been applied and tested for various types of music. However, the concept is yet to be tested on more specific genres of music such as traditional Scottish music, for which extensive collections are not readily available. Hence exploring the capabilities of a Transfer Learning (TL) approach on these types of music is an interesting challenge for the methodology. The curated set of MIDI Scottish melodies was preprocessed in order to obtain the same number of tracks used in the BinaryMuseGAN model; converted into pianoroll format and then used as training set to fine tune a pretrained model, generated from the Lakh MIDI dataset. The results obtained have been compared with the results obtained by training the same GAN model from scratch on the sole Scottish music dataset. Results are presented in terms of variation and average performances achieved at different epochs for five performance metrics, three adopted from the Lakh dataset (qualified note rate, polyphonicity, tonal distance) and two custom defined to highlight Scottish music characteristics (dotted rhythm and pentatonic note). From these results, the TL method shows to be more effective, with lower number of epochs, to converge stably and closely to the original dataset reference metrics values.
Original language | English |
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Title of host publication | Artificial Intelligence in Music, Sound, Art and Design |
Subtitle of host publication | 10th International Conference, EvoMUSART 2021, Held as Part of EvoStar 2021, Virtual Event, April 7–9, 2021, Proceedings |
Editors | Juan Romero, Tiago Martins, Nereida Rodríguez-Fernández |
Place of Publication | Cham |
Publisher | Springer |
Pages | 187-202 |
Number of pages | 16 |
ISBN (Electronic) | 9783030729141 |
ISBN (Print) | 9783030729134 |
DOIs | |
Publication status | Published - 17 May 2021 |
Event | 10th International Conference on Artificial Intelligence in Music, Sound, Art and Design - Virtual, Seville, Spain Duration: 7 Apr 2021 → 9 Apr 2021 http://www.evostar.org/2021/evomusart/ |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 12693 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 10th International Conference on Artificial Intelligence in Music, Sound, Art and Design |
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Abbreviated title | EvoMUSART |
Country/Territory | Spain |
City | Seville |
Period | 7/04/21 → 9/04/21 |
Internet address |
Keywords
- generative adversarial network
- transfer learning
- convolutional neural network
- Scottish music
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Dive into the research topics of 'Convolutional generative adversarial network, via transfer learning, for traditional Scottish music generation'. Together they form a unique fingerprint.Datasets
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Data for: "Convolutional Generative Adversarial Network, via Transfer Learning, for Traditional Scottish Music Generation"
Marchetti, F. (Creator), Wilson, C. (Creator), Powell, C. (Creator), Minisci, E. (Creator) & Riccardi, A. (Creator), University of Strathclyde, 25 Mar 2021
DOI: 10.15129/4ae2eb7e-678d-4644-90ad-1cf2a953287f, http://www.evostar.org/2021/evomusart/
Dataset
Activities
- 1 Participation in conference
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10th International Conference on Artificial Intelligence in Music, Sound, Art and Design
Marchetti, F. (Participant)
7 Apr 2021 → 9 Apr 2021Activity: Participating in or organising an event types › Participation in conference