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http://dx.doi.org/10.9717/kmms.2018.21.3.391

Performance Comparison of Guitar Chords Classification Systems Based on Artificial Neural Network  

Park, Sun Bae (Dept. of Electronic and Electrical Engineering., Graduate School, Hongik University)
Yoo, Do-Sik (Dept. of Electronic and Electrical Engineering., Graduate School, Hongik University)
Publication Information
Journal of Korea Multimedia Society / v.21, no.3, 2018 , pp. 391-399 More about this Journal
Abstract
In this paper, we construct and compare various guitar chord classification systems using perceptron neural network and convolutional neural network without pre-processing other than Fourier transform to identify the optimal chord classification system. Conventional guitar chord classification schemes use, for better feature extraction, computationally demanding pre-processing techniques such as stochastic analysis employing a hidden markov model or an acoustic data filtering and hence are burdensome for real-time chord classifications. For this reason, we construct various perceptron neural networks and convolutional neural networks that use only Fourier tranform for data pre-processing and compare them with dataset obtained by playing an electric guitar. According to our comparison, convolutional neural networks provide optimal performance considering both chord classification acurracy and fast processing time. In particular, convolutional neural networks exhibit robust performance even when only small fraction of low frequency components of the data are used.
Keywords
Audio Signal Processing; Artificial Neural Network; Convolutional Neural Network; Pattern Recognition;
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Times Cited By KSCI : 1  (Citation Analysis)
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