  Music genre classification is one example of content-based analysis of music
signals. Traditionally, human-engineered features were used to automatize this
task and 61% accuracy has been achieved in the 10-genre classification.
However, it's still below the 70% accuracy that humans could achieve in the
same task. Here, we propose a new method that combines knowledge of human
perception study in music genre classification and the neurophysiology of the
auditory system. The method works by training a simple convolutional neural
network (CNN) to classify a short segment of the music signal. Then, the genre
of a music is determined by splitting it into short segments and then combining
CNN's predictions from all short segments. After training, this method achieves
human-level (70%) accuracy and the filters learned in the CNN resemble the
spectrotemporal receptive field (STRF) in the auditory system.
