Chord recognition using neural networks based on Particle Swarm Optimization
Cheng‐Jian Lin, Chin‐Ling Lee, Chun‐Cheng Peng · 2011
A sequence of musical chords can facilitate musicians in music arrangement and accompaniment. To implement an intelligent system for chord recognition, in this paper we propose a novel approach using Artificial Neural Networks (ANN) trained by the Particle Swarm Optimization (PSO) technique and Backpropagation (BP) learning algorithm. All the training and testing data are generated from Musical Instrument Digital Interface (MIDI) symbolic data. Furthermore, in order to improve the recognition efficiency, the cadence is also included as an additional feature. Cadence is the structural punctuation of a melodic phrase and it is considered as an important feature for chord recognition. Experimental results of our proposed approach show that the addition of this feature improves significantly the recognition rate, and also that the ANN-PSO method outperforms ANN-BP in chord recognition. In addition, since preliminary experimental recognition rates are generally not stable enough, we further choose the optimal ANNs to propose a two-phase ANN model to ensemble the recognition results.