FifthNet: Structured Compact Neural Networks for Automatic Chord Recognition

Abstract

Deep learning has become popular for many music processing tasks with Convolutional Neural Networks (CNNs) often applied. CNNs can be computationally expensive, a problem that may be alleviated through design of compact network elements or by compressing trained networks. CNNs assemble high-level structure in a hierarchical fashion, starting from small simple local patterns. On the other hand, much structure found in music spectra, such as harmonicity, is already well-defined. Both signal representations and processing methods have previously exploited such structure. We propose FifthNet, a compact neural network that is applied to the task of Automatic Chord Recognition (ACR). The compactness of FifthNet is effected through exploiting known data structure; first by arranging the network inputs according to expected data structures, then by separating processing of the semantically meaningful dimensions of the data. FifthNet is then seen to perform similar to a state-of-the-art CNN for ACR while employing only a small percentage of the parameters and computational expense used by the CNN.