Music Genre Classification using Machine Learning Techniques
This addresses a challenge in music information retrieval for categorizing music files, but it is incremental as it combines existing approaches.
The paper tackled music genre classification by comparing a CNN trained on spectrograms with traditional classifiers using hand-crafted features, achieving an AUC of 0.894 with an ensemble method.
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.