Earticle

초록

영어

Speech Signals have high range of variation in amplitudes and frequency. These acoustic signals with diverse properties are hard to recognize and filter if mixed with noise. To separate noise from original signal, the artifact peaks are separated from original signal and discarded. In this paper, the ICA method of signal denoising is used to differentiate the speech signal from periodic noise and Empirical Mode Decomposition method is proposed to generate the components of signal. The IMF(s) of signal is the non-linear descending order of frequency components that have been filtered for better SNR. Filtering with wiener filter has amended output but also results in loss of information. The selection of IMF(s) for signal regeneration when optimized using objective function of PSO, the information of original signal was dramatically preserved with suppressed noise. The system is tested on 4 example signals and proposed technique illustrates lower mean square error and higher SNR compared to wiener and ICA.

목차

Abstract
 1. Introduction
 2. Proposed method
 3. Empirical mode decomposition
 4. Particle swarm optimization
 5. Experimental Setup
  5.1 Evaluation Parameters
  5.2 Test Inputs
 6. Results
 7. Conclusion
 References

키워드

저자

• Lakshmikanth S. [ Research scholar, Jain University, India ]
• Natraj K. R. [ Professor, SJBIT, India ]
• Rekha K. R. [ Professor, SJBIT, India ]

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