The Journal of the Acoustical Society of Korea (한국음향학회지)

The Acoustical Society of Korea (한국음향학회)
권호 표지
DOI QR코드

DOI QR Code

Sound event classification using deep neural network based transfer learning

깊은 신경망 기반의 전이학습을 이용한 사운드 이벤트 분류

  • 임형준 (한국과학기술원 전기및전자공학부) ;
  • 김명종 (한국과학기술원 전기및전자공학부) ;
  • 김회린 (한국과학기술원 전기및전자공학부)
  • Received : 2015.11.10
  • Accepted : 2015.12.22
  • Published : 2016.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

Deep neural network that effectively capture the characteristics of data has been widely used in various applications. However, the amount of sound database is often insufficient for learning the deep neural network properly, so resulting in overfitting problems. In this paper, we propose a transfer learning framework that can effectively train the deep neural network even with insufficient sound event data by employing rich speech or music data. A series of experimental results verify that proposed method performs significantly better than the baseline deep neural network that was trained only with small sound event data.

깊은 신경망은 데이터의 특성을 효과적으로 나타낼 수 있는 방법으로 최근 많은 응용 분야에서 활용되고 있다. 하지만, 제한적인 양의 데이터베이스는 깊은 신경망을 훈련하는 과정에서 과적합 문제를 야기할 수 있다. 본 논문에서는 풍부한 양의 음성 혹은 음악 데이터를 이용한 전이학습을 통해 제한적인 양의 사운드 이벤트에 대한 깊은 신경망을 효과적으로 훈련하는 방법을 제안한다. 일련의 실험을 통해 제안하는 방법이 적은 양의 사운드 이벤트 데이터만으로 훈련된 깊은 신경망에 비해 현저한 성능 향상이 있음을 확인하였다.

Keywords

References

  1. G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury, "Deep neural networks for acoustic modeling in speech recognition," IEEE Signal Process. Mag. 29, 82-97 (2012).
  2. G. E. Dahl, D. Yu, L. Deng, and A. Acero, "Context-dependent pre-trained deep neural networks for large vocabulary speech recognition," IEEE Trans. Audio, Speech, and Lang. Process. 20, 33-42 (2012).
  3. C. Weng, D. Yu, S. Watanabe, and B. H. F. Juang, "Recurrent deep neural networks for robust speech recognition," in Proc. IEEE ICASSP, 5532-5536 (2014).
  4. Y. Lei, N. Scheffer, L. Ferrer, and M. McLaren, "A novel scheme for speaker recognition using a phonetically-aware deep neural network," in Proc. IEEE ICASSP, 1695-1699 (2014).
  5. D. G. Romero and A. McCree, "Insight into deep neural networks for speaker recognition," in Proc. Interspeech, 1141-1145 (2015).
  6. S. J. Pan and Q. Yang, "A survey on transfer learning," IEEE Trans. Knowl. Data Eng. 22, 1345-1359 (2010). https://doi.org/10.1109/TKDE.2009.191
  7. L. Deng and X. Li, "Machine learning paradigms for speech recognition: An overview," IEEE Trans. Audio, Speech, Lang. Process. 21, 1060-1089 (2013). https://doi.org/10.1109/TASL.2013.2244083
  8. A. Das and M. Hasegawa-Johnson, "Cross-lingual transfer learning during supervised training in low resource scenarios," in Proc. Interspeech, 3531-3535 (2015).
  9. J. T. Huang, J. Li, D. Yu, L. Deng, and Y. Gong, "Crosslanguage knowledge transfer using multilingual deep neural network with shared hidden layers," in Proc. IEEE ICASSP, 7304-7308 (2013).
  10. O. Gencoglu, T. Virtanen, and H. Huttunen, "Recognition of acoustic events using deep neural networks," in Proc. IEEE European Signal Process. Conf, 506-510 (2014).
  11. M. Espi, M. Fujimoto, K. Kinoshita, and T. Nakatani, "Feature extraction strategies in deep learning based acoustic event detection," in Proc. Interspeech, 2922-2926 (2015).
  12. S. Nakamura, K. Hiyane, F. Asano, T. Yamada, and T. Endo, "Data collection in real acoustical environments for sound scene understanding and hands-free speech recognition," in Proc. Eurospeech, 2255-2258 (1999).
  13. P. Price, W. M. Fisher, J. Bernstein, and D. S. Pallett, "The DARPA 1000-word resource management database for continuous speech recognition," in Proc. IEEE ICASSP, 651-654 (1988).
  14. G. Tzanetakis and P. Cook, "Musical genre classification of audio signals," IEEE Trans. Audio, Speech and Lang. Process. 10, 293-302 (2002). https://doi.org/10.1109/TSA.2002.800560
  15. Y. Miao, "Kaldi+PDNN: building DNN-based ASR systems with Kaldi and PDNN," arXiv:1401.6984, (2014).
  16. J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, "How transferable are features in deep neural networks?" in Proc. Neural Inform. Process. Syst., 3320-3328 (2014).