Title: Transforming Musical Signals through a Genre Classifying Convolutional Neural Network,
Abstract: Convolutional neural networks (CNNs) have been successfully applied on both
discriminative and generative modeling for music-related tasks. For a
particular task, the trained CNN contains information representing the decision
making or the abstracting process. One can hope to manipulate existing music
based on this 'informed' network and create music with new features
corresponding to the knowledge obtained by the network. In this paper, we
propose a method to utilize the stored information from a CNN trained on
musical genre classification task. The network was composed of three
convolutional layers, and was trained to classify five-second song clips into
five different genres. After training, randomly selected clips were modified by
maximizing the sum of outputs from the network layers. In addition to the
potential of such CNNs to produce interesting audio transformation, more
information about the network and the original music could be obtained from the
analysis of the generated features since these features indicate how the
network 'understands' the music.