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A. Schindler, T. Lidy, A. Rauber:
"Comparing shallow versus deep neural network architectures for automatic music genre classification";
Vortrag: 9th Forum Media Technology (FMT2016), St. Pölten, Austria; 23.11.2016 - 24.11.2016; in: "Proceedings of the 9th Forum Media Technology (FMT2016)", St. Pölten University of Applied Sciences, Institute of Creative\Media/Technologies, (2016), ISBN: 9781326881184; 5 S.

In this paper we investigate performance differences of different neural network architectures on the task of automatic music genre classification. Comparative evaluations on four well known datasets of different sizes were performed including the application of two audio data augmentation methods. The results show that shallow network architectures are better suited for small datasets than deeper models, which could be relevant for experiments and applications which rely on small datasets. A noticeable advantage was observed through the application of data augmentation using deep models. A final comparison with previous evaluations on the same datasets shows that the presented neural network based approaches already outperform state-of-the-art handcrafted music features.

Neural networks, Deep learning, Classification, Music, Audio, Convolutional Neural Networks, Neural Network Architectures

http://publik.tuwien.ac.at/files/publik_256008.pdf