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Music Genre Classification using Machine Learning Techniques
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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.
Forward citations
Cited by 2 Pith papers
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Comparison of spectrogram scaling in multi-label Music Genre Recognition
On a custom 18k-song multi-label dataset, Mel-scaled spectrograms outperform standard spectrograms for music genre classification with transfer-learned ResNets.
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Music Genre Classification Using Machine Learning Techniques
A GTZAN comparison whose own results table contradicts its claim that SVM beats CNN.
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