Improving Pitch Class Profile for Musical Chords Recognition Combining Major Chord Filters and Convolution Neural Networks

In this paper, we consider the challenging problem of music recognition and present an effective deep learning based method using a convolution neural network for chord recognition. It has known that a pitch class profile (PCP) is the commonly signal representation of musical harmonic analysis. However, the PCP vector is not expressive enough for chord recognition, which often occurs in many real-world environments. In this study, we extend the PCP vector scheme to address the limitation. Our proposed method basically consists of two major steps. First, we introduce novel filters and apply then to PCP vector to transform the vector into membership of 7 major chords as features to represent the input matrix. The second step is to efficiently learning feature on the transformed matrix (2D-PCP) using convolution neural network. We propose a trainable, data-driven approach that automatically learns features and its classifier simultaneously. Experimental results conducted on the task of musical chords recognition that the proposed method achieves improvements of classification accuracy more than 40% in accuracy in comparing with based line methods.