  In music, short-term features such as pitch and tempo constitute long-term
semantic features such as melody and narrative. A music genre classification
(MGC) system should be able to analyze these features. In this research, we
propose a novel framework that can extract and aggregate both short- and
long-term features hierarchically. Our framework is based on ECAPA-TDNN, where
all the layers that extract short-term features are affected by the layers that
extract long-term features because of the back-propagation training. To prevent
the distortion of short-term features, we devised the convolution channel
separation technique that separates short-term features from long-term feature
extraction paths. To extract more diverse features from our framework, we
incorporated the frequency sub-bands aggregation method, which divides the
input spectrogram along frequency bandwidths and processes each segment. We
evaluated our framework using the Melon Playlist dataset which is a large-scale
dataset containing 600 times more data than GTZAN which is a widely used
dataset in MGC studies. As the result, our framework achieved 70.4% accuracy,
which was improved by 16.9% compared to a conventional framework.
