Optimizing Fractional-Order Convolutional Neural Networks for Groove Classification in Music Using Differential Evolution

Optimizing Fractional-Order Convolutional Neural Networks for Groove Classification in Music Using Differential Evolution

This study presents a differential evolution (DE)-based optimization approach for fractional-order convolutional neural networks (FOCNNs) aimed at enhancing the accuracy of groove classification in music. Groove, an essential element in music perception, is typically influenced by rhythmic patterns...

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Bibliographic Details
Main Authors: Jiangang Chen, Pei Su, Daxin Li, Junbo Han, Gaoquan Zhou, Donghui Tang
Format: Article
Language:English
Published: MDPI AG 2024-10-01
Series:Fractal and Fractional
Subjects:
fractional-order convolutional neural networks
differential evolution
groove classification
music information retrieval
optimization algorithms
Online Access:https://www.mdpi.com/2504-3110/8/11/616
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