2022
DOI: 10.1007/s42979-022-01490-6
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Genre Recognition from Symbolic Music with CNNs: Performance and Explainability

Abstract: In this work, we study the use of convolutional neural networks for genre recognition in symbolically represented music. Specifically, we explore the effects of changing network depth, width and kernel sizes while keeping the number of trainable parameters and each block’s receptive field constant. We propose an architecture for handling MIDI data that makes use of multiple resolutions of the input, called Multiple Sequence Resolution Network (MuSeReNet). These networks accept multiple inputs, each at half the… Show more

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