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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 blackbox. 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 severaldoi:10.1109/cbmi.2016.7500246 dblp:conf/cbmi/PonsLS16 fatcat:yfnqfa6lpnefnp2fr7ad7ektkm