  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.
