Holistic Approaches to Music Genre Classification using Efficient Transfer and Deep Learning Techniques

作者：

Highlights：

• Proposed Weighted Visibility Graph based Elastic Net Sparse Classification.

• Proposed Sequential Machine Learning Analysis with Stacked Denoising Autoencoder.

• Proposed Riemannian Alliance based Tangent Space Mapping Transfer Learning.

• Proposed a BAG deep learning model.

• Proposed works are implemented for three music datasets.

摘要

•Proposed Weighted Visibility Graph based Elastic Net Sparse Classification.•Proposed Sequential Machine Learning Analysis with Stacked Denoising Autoencoder.•Proposed Riemannian Alliance based Tangent Space Mapping Transfer Learning.•Proposed a BAG deep learning model.•Proposed works are implemented for three music datasets.

论文关键词：Music classification,Machine learning,Transfer learning,Deep learning,Multiclass classification

论文评审过程：Received 15 February 2022, Revised 4 August 2022, Accepted 17 August 2022, Available online 22 August 2022, Version of Record 24 August 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.118636