Music Artist Classification With Convolutional Recurrent Neural Networks

Abstract

Previous attempts at music artist classification use frame level audio features which summarize frequency content within short intervals of time. Comparatively, more recent music information retrieval tasks take advantage of temporal structure in audio spectrograms using deep convolutional and recurrent models. This paper revisits artist classification with this new framework and empirically explores the impacts of incorporating temporal structure in the feature representation. To this end, an established classification architecture, a Convolutional Recurrent Neural Network (CRNN), is applied to the artist20 music artist identification dataset under a comprehensive set of conditions. These include audio clip length, which is a novel contribution in this work, and previously identified considerations such as dataset split and feature level. Our results improve upon baseline works, verify the influence of the producer effect on classification performance and demonstrate the trade-offs be

Stats

• citations38
• S2 citations—
• github stars0
• HF likes0
• heat score11.93
• arxiv keynasrullah2019music

Related papers

• Convolutional Recurrent Neural Networks For Music Classification (2016)18.98
• Spectral And Rhythm Features For Audio Classification With Deep Convolutional Neural Networks (2024)0.00
• Explaining Deep Convolutional Neural Networks On Music Classification (2016)0.00
• Genre-agnostic Key Classification With Convolutional Neural Networks (2018)0.00
• Rethinking Recurrent Latent Variable Model For Music Composition (2018)7.50
• Multi-level And Multi-scale Feature Aggregation Using Pre-trained Convolutional Neural Networks For Music Auto-tagging (2017)15.43
• Convolution Channel Separation And Frequency Sub-bands Aggregation For Music Genre Classification (2022)0.00
• Audio-based Music Classification With Densenet And Data Augmentation (2019)10.48