Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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Performance Comparison of Guitar Chords Classification Systems Based on Artificial Neural Network

인공신경망 기반의 기타 코드 분류 시스템 성능 비교

  • 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)
  • Received : 2017.12.08
  • Accepted : 2018.02.10
  • Published : 2018.03.31
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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.

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References

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