Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Features
This work addresses a niche problem for music information retrieval researchers by improving subgenre classification in EDM, but it is incremental as it builds on existing methods.
The paper tackled the challenging problem of classifying Electronic Dance Music (EDM) subgenres by extending a state-of-the-art music auto-tagging model with tempo-related features and fusion strategies, achieving higher classification accuracy on a dataset of 75,000 songs across 30 subgenres.
Along with the evolution of music technology, a large number of styles, or "subgenres," of Electronic Dance Music(EDM) have emerged in recent years. While the classification task of distinguishing between EDM and non-EDM has been often studied in the context of music genre classification, little work has been done on the more challenging EDM subgenre classification. The state-of-art model is based on extremely randomized trees and could be improved by deep learning methods. In this paper, we extend the state-of-art music auto-tagging model "short-chunkCNN+Resnet" to EDM subgenre classification, with the addition of two mid-level tempo-related feature representations, called the Fourier tempogram and autocorrelation tempogram. And, we explore two fusion strategies, early fusion and late fusion, to aggregate the two types of tempograms. We evaluate the proposed models using a large dataset consisting of 75,000 songs for 30 different EDM subgenres, and show that the adoption of deep learning models and tempo features indeed leads to higher classification accuracy.