Notion meta-learner: A technique for few-shot learning in music genre recognition

Jinhong Shi; F. Hernando-Galego; Diego Martín; Mohammad Khishe

doi:10.1016/j.entcom.2025.100961

Publication record · 2025

Notion meta-learner: A technique for few-shot learning in music genre recognition

Authors: Jinhong Shi · F. Hernando-Galego · Diego Martín · Mohammad Khishe
Year: 2025
Publisher: Elsevier
Journal or publication: Entertainment Computing
Google Scholar citations: 6

Journal indicators

CiteScore 2025 (Scopus)5.1

Best quartileQ2

Top 10% in any categoryNo

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Software197/503 · Q2 · percentile 60
Human-Computer Interaction95/196 · Q2 · percentile 51

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JCR (Clarivate): Not verified: the supplied ranking file is Scopus CiteScore, not JCR.

Not identified in the supplied report on non-standard bibliometric behaviour (2017–2019).

The report is an independent analysis published in 2021; it is not an official ANECA ban or endorsement list.

Research summary

The Notion Meta-Learner maps human-understandable musical notions into partially organised metric spaces, improving generalisation and interpretability for one-shot and five-shot music-genre recognition.

Original description prepared for this website; consult the publication for its authoritative abstract.

DOI 10.1016/j.entcom.2025.100961 ↗ · View Google Scholar record ↗