한국멀티미디어학회논문지 (Journal of Korea Multimedia Society)

  • 제21권10호
  • /
  • Pages.1195-1202
  • /
  • 2018
  • /
  • 1229-7771(pISSN)
  • /
  • 2384-0102(eISSN)
한국멀티미디어학회 (Korea Multimedia Society)
권호 표지
DOI QR코드

DOI QR Code

전이학습을 이용한 효율적인 기타코드 분류 시스템

An Efficient Guitar Chords Classification System Using Transfer Learning

  • Park, Sun Bae (Dept. of Electronic and Electrical Engineering., Graduate School, Hongik University) ;
  • Lee, Ho-Kyoung (Dept. of Electronic and Electrical Engineering., Graduate School, Hongik University) ;
  • Yoo, Do Sik (Dept. of Electronic and Electrical Engineering., Graduate School, Hongik University)
  • 투고 : 2018.07.25
  • 심사 : 2018.09.27
  • 발행 : 2018.10.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Artificial neural network is widely used for its excellent performance and implementability. However, traditional neural network needs to learn the system from scratch, with the addition of new input data, the variation of the observation environment, or the change in the form of input/output data. To resolve such a problem, the technique of transfer learning has been proposed. Transfer learning constructs a newly developed target system partially updating existing system and hence provides much more efficient learning process. Until now, transfer learning is mainly studied in the field of image processing and is not yet widely employed in acoustic data processing. In this paper, focusing on the scalability of transfer learning, we apply the concept of transfer learning to the problem of guitar chord classification and evaluate its performance. For this purpose, we build a target system of convolutional neutral network (CNN) based 48 guitar chords classification system by applying the concept of transfer learning to a source system of CNN based 24 guitar chords classification system. We show that the system with transfer learning has performance similar to that of conventional system, but it requires only half the learning time.

키워드

참고문헌

  1. A.M. Barbancho, A. Klapuri, L.J. Tardon, and I. Barbancho, "Automatic Transcription of Guitar Chords and Fingering from Audio," IEEE Transactions on Audio, Speech, and Language Processing, Vol. 20, No. 3, pp. 915-921. 2012. https://doi.org/10.1109/TASL.2011.2174227
  2. A. Sheh and D. Ellis, "Chord Segmentation and Recognition Using EM-trained Hidden Markov Models," Proceedings of the 4th International Society for Music Information Retrieval Conference, pp. 183-189, 2006.
  3. J. Guerrero-Turrubiates, S. Ledsema, S. Conzalez-Reyna, and G. Avina-Cervates. "Guitar Chords Classification Using Uncertainty Measurements of Frequency Bins," Mathematical Problems in Engineering, Vol. 2015, Article ID. 205369, pp. 1-9, 2015.
  4. S. Arun and Y. Wang. "Key, Chord, and Rhythm Tracking of Popular Music Recordings," Computer Music Journal, Vol. 29, No. 3, pp. 75-86, 2005. https://doi.org/10.1162/0148926054798205
  5. S.B Park and D.S Yoo, "Performance Comparison of Guitar Chords Classification Systems Based on Artificial Neural Network," Journal of Korea Multimedia Society, Vol. 21, No. 3, pp. 391-399, 2018. https://doi.org/10.9717/KMMS.2018.21.3.391
  6. S.J. Pan and Q. Yang. "A Survey on Transfer Learning." IEEE Transactions on Knowledge and Data Engineering, Vol. 22, No. 10, pp. 1345-1359, 2010. https://doi.org/10.1109/TKDE.2009.191
  7. H.C. Shin, H.R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, et al., "Deep Convolutional Neural Networks for Computer-aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning," IEEE Transactions on Medical Imaging, Vol. 35, No. 5, pp. 1285-1298, 2016. https://doi.org/10.1109/TMI.2016.2528162
  8. M. Oquab, L. Bottou, I. Laptev, and J. Sivic, "Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1717-1724, 2014.
  9. D.C. Ciresan, U. Meier, and J. Schmidhuber, "Transfer Learning for Latin and Chinese Characters with Deep Neural Networks," Proceeding of the 2012 International Joint Conference on Neural Networks, pp. 1-6, 2012.
  10. S.A Prajapati, R. Nagaraj, and S. Mitra, "Classification of Dental Diseases using CNN and Transfer Learning," Proceeding of 2017 5th International Symposium on Computational and Business Intelligence, pp. 70-74, 2017.
  11. Y. LeCun, B.D. Boser, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, et al., "Backpropagation Applied to Handwritten Zip Code Recognition," Neural Computation, Vol. 1, No. 4, pp. 541-551, 1989. https://doi.org/10.1162/neco.1989.1.4.541
  12. J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, et al., "Recent Advances in Convolutional Neural Networks," ArXiv Preprint arXiv:1512.07108, 2015.
  13. Music Theory/Chords. https://en.wikibooks.org/wiki/Music_Theory/Chords (accessed Aug., 12, 2018).
  14. A.C. Timothy, "Random Forests, Decision Trees, and Categorical Predictors: The" Absent Levels" Problem," ArXiv Preprint ArXiv: 1706.03492, 2017.
  15. M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, et al., "Tensorflow: Large-scale Machine Learning on Heterogeneous Distributed Systems." ArXiv Preprint ArXiv: 1603.04467, 2016.
  16. P.K. Diederik and B. Jimmy, "Adam: A Method for Stochastic Optimization," ArXiv Preprint ArXiv:1412.6980, 2014.