Categorization of music plays an essential role in music appreciation and cognition. A study shows that genre is so important to listeners that the style of a piece can influence their liking for it more than the piece itself [1, 2]. The problem of recognizing song genres, however, is a challenging task as song genres are subjective in nature as there are no clear-cut boundaries between human-labeled song genres.
Multiple researches have shown that machine learning approaches have the potential to achieve significant results in this problem. However, we believe that it is possible to further explore the potential of applying deep learning approach on the music genre classification problem. While other works have aimed to adopt and assess deep learning methods that have been shown to be effective in other domains, there is still a great need for more original research focusing on music primarily and utilizing musical knowledge and insight .
For reproducibility, we published our experiment worksheet on CodaLab. This contains our introduction to the problem, the datasets, code, and other artifacts from our various experiments.
 H.G. Tekman, N. Hortacsu. Aspects of stylistic knowledge: what are different styles like and why do we listen to them? Psychol. Music, 30 (1) (2002), pp. 28-47.
 A.C. North, D.J. Hargreaves. Liking for musical styles. Music Scientae, 1 (1) (1997), pp. 109-128.
 K. Choi, G. Fazekas et al. A Tutorial on Deep Learning for Music Information Retrieval. arXiv: 1709.04396. 13 Sep 2017.