CHORD RECOGNITION USING NEURAL NETWORKS BASED ON PARTICLE SWARM OPTIMIZATION

A sequence of musical chords can facilitate musicians in music arrangement and accompaniment. To implement an intelligent system for chord recognition, in this article we propose a novel approach using artificial neural networks (ANN) trained bythe particle swarm optimization (PSO) technique and back-propagation (BP) learning algorithm. All of the training and testing data are generated from musical instrument digital interface (MIDI) symbolic data. Furthermore, in order to improve the recognition efficiency, an additional feature of cadencesis included. In other words, cadence is not only the structural punctuation of a melodic phrase but is considered as the important feature for chord recognition. Experimental results of our proposed approach show that adding a cadence feature significantly improves recognition rate, and the ANN-PSO method outperforms ANN-BP in chord recognition. In addition, because preliminary experimental recognition rates are generally not stable enough, we chose the optimal ANNs to propose a two-phase ANN model to integrate the results among many models.