RT info:eu-repo/semantics/article
T1 Notion meta-learner: A technique for few-shot learning in music genre recognition
A1 Shi, Jinhong
A1 Hernando Gallego, Francisco
A1 Martín De Andrés, Diego
A1 Khishe, Mohammad
K1 Inteligencia artificial
K1 Música
K1 Procesamiento de datos
K1 Acústica
K1 Metaaprendiz de nociones
K1 Reconocimiento de géneros musicales
K1 Nociones de alto nivel
K1 Aprendizaje con pocos ejemplos
K1 1203 Ciencia de Los Ordenadores
K1 2201 Acústica
AB This paper presents the notion of meta-learner (NML), an innovative meta-learning methodology designed to enhance the performance of few-shot learning (FSL) regarding the recognition of music genres. Current FSL techniques frequently encounter difficulties due to the absence of organized representations and low capacity for generalization, which impede their efficacy in practical scenarios. The NML meta-learner overcomes these obstacles by acquiring the ability to learn across notion dimensions that humans can understand, thus improving its capacity for generalization and interpretability. Instead of gaining knowledge in a combined and disorganized metric space, the notion meta-learner acquires knowledge by mapping high-level notions into partially organized metric spaces. This technique allows for the efficient integration of several notion learners. We assessed the performance of NMLFSL by utilizing the GTZAN dataset and comparing employing seven different benchmarks. The experimental outcomes show that the NML performs superior to current FSL approaches in tasks that include recognizing music genres with only one or five examples, thereby demonstrating its potential to improve the current state of the art in this field. In addition, ablation experiments assess the influence of essential variables, offering valuable information about the effectiveness of the suggested method. NMLFSL is a notable advancement in using meta-learning to enhance the reliability and precision of music genre recognition (MGR) systems.
PB Elsevier
SN 1875-9521
YR 2025
FD 2025
LK https://uvadoc.uva.es/handle/10324/83847
UL https://uvadoc.uva.es/handle/10324/83847
LA eng
NO Entertainment Computing, 2025, vol. 54, artículo 100961.
NO Producción Científica
DS UVaDOC
RD 28-mar-2026