Music Genre Classification using Machine Learning Techniques

Hareesh Bahuleyan
4/3/2018
cs.SDeess.AS

Official code: HareeshBahuleyan/music-genre-classification (101 stars, MIT, TensorFlow, last commit 2021-05-23).

Abstract

Categorizing music files according to their genre is a challenging task in the area of music information retrieval (MIR). In this study, we compare the performance of two classes of models. The first is a deep learning approach wherein a CNN model is trained end-to-end, to predict the genre label of an audio signal, solely using its spectrogram. The second approach utilizes hand-crafted features, both from the time domain and the frequency domain. We train four traditional machine learning classifiers with these features and compare their performance. The features that contribute the most towards this multi-class classification task are identified. The experiments are conducted on the Audio set data set and we report an AUC value of 0.894 for an ensemble classifier which combines the two proposed approaches.

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Code Implementations(1)

HareeshBahuleyan/music-genre-classificationOfficial100%
TensorFlowMIT

Recognizing the genre of music files using machine learning and deep learning models

10138Python, Jupyter NotebookMar 24, 20185 years agoMIT
cnngenre-classificationmachine-learningmusictensorflow+1 more

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Cite this paper

@article{bahuleyan2018music,
  title  = {Music Genre Classification using Machine Learning Techniques},
  author = {Hareesh Bahuleyan},
  year   = {2018},
  eprint = {1804.01149},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1804.01149}
}

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