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J. Pons, T. Lidy, X. Serra:
"Experimenting with musically motivated convolutional neural networks";
Talk: 14th International Workshop on Content-based Multimedia Indexing, Bukarest, Rumänien; 2016-06-15 - 2016-06-17; in: "Proceedings of the 14th International Workshop on Content-based Multimedia Indexing (CBMI 2016)", IEEE, (2016), ISBN: 978-1-4673-8695-1.

A common criticism of deep learning relates to
the difficulty in understanding the underlying relationships that
the neural networks are learning, thus behaving like a black-
box. In this article we explore various architectural choices of
relevance for music signals classification tasks in order to start
understanding what the chosen networks are learning. We first
discuss how convolutional filters with different shapes can fit
specific musical concepts and based on that we propose several
musically motivated architectures. These architectures are then
assessed by measuring the accuracy of the deep learning model
in the prediction of various music classes using a known dataset
of audio recordings of ballroom music. The classes in this dataset
have a strong correlation with tempo, what allows assessing if
the proposed architectures are learning frequency and/or time
dependencies. Additionally, a black-box model is proposed as a
baseline for comparison. With these experiments we have been
able to understand what some deep learning based algorithms
can learn from a particular set of data.

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

http://dx.doi.org/10.1109/CBMI.2016.7500246

http://publik.tuwien.ac.at/files/publik_255991.pdf