Earticle

초록

영어

To solve the difficulty in recognition coal-rock interface for the top coal caving process, we proposed a new method based on Mel-frequency cepstrum coefficient (MFCC) and neural network. In this paper, we conducted the noise separation by Independent Component Analysis (ICA) for acoustic signal. Then we extracted MFCC as the feature and recognized the coal-rock interface via BP neural network. The result shows that MFCC reflect the voice features of coal-rock more effectively, comparing to other features (frame energy and kurtosis), it provides average relative reductions of 12% and 19% in error rate, which recognition rate is 83%. We conclude that the method based on MFCC and neural network is an effectively and automatically detection for the coal-rock interface recognitions.

목차

Abstract
 1. Introduction
 2. Noise Separate
 3. Feature Extraction
  3.1. Mel Cepstrum Coefficient
  3.2. Kurtosis
  3.3. Frame Energy
 4. Coal-rock Interface Recognition based on the Neural Network
 5. Experimental Result
 6. Conclusion
 Acknowledgement
 References

키워드

저자

• Xu Junkai [ School of Mechanical Engineering, Shandong University ]
• Wang Zengcai2 [ School of Mechanical Engineering, Shandong University ]
• Zhang Wanzhi [ School of Mechanical Engineering, Shandong University ]
• He Yanpeng [ School of Mechanical Engineering, Shandong University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605