The week's pick
Reseach Article
Correlation based Transient Removal Method for Speech Signal Enhancement
| Communications on Applied Electronics |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 5 - Number 6 |
| Year of Publication: 2016 |
| Authors: Pushpraj Tanwar, Ajay Somkuwar |
10.5120/cae2016652311
|
Pushpraj Tanwar, Ajay Somkuwar . Correlation based Transient Removal Method for Speech Signal Enhancement. Communications on Applied Electronics. 5, 6 ( Jul 2016), 16-19. DOI=10.5120/cae2016652311
Abstract
In this article, unwanted transients are specified by correlating the previous shade power standards and eliminated the detected transients. Various studies have been done on the autocorrelation based method to perform the noise reduction in speech signals. The speech signal is a one dimensional signal, for that the correlation may be with its delayed function. The proposed method uses recursive approach and the autocorrelation coefficient as a constraint or stopping criterion. The algorithm solves the transient problem of threshold for transient reduction and provides the alternative. The final simulation modelling shows the results.
References
- S. Ikbul, H. Misra, H. Bourlurd, “Phase Autocorrelation (PAC) Derived Robust Speech Features” IEEE, ICASSP 2003, pp. 134-136.
- R. Nawaz and J. A. Chambers, “A novel single lag auto-correlation minimization (slam) algorithm for blind adaptive channel shortening” IEEE, ICASSP 2005, pp. III- 885-888.
- Z. Shuyin, G. Ying, W. Buhong “Auto-correlation Property of Speech and Its Application in Voice Activity Detection” International Workshop on Education Technology and Computer Science 2009, pp. 265-268.
- G. Faraham, S. M. Ahadi, and M. M. Homayounpour, “Improved autocorrelation based noise robust speech recognition using kernel based cross correlation and overestimation parameters” EUSIPCO 2007, pp. 2355-2359.
- A. Unnisa and A. K. Dwivedi, “Noise Analysis on Auto Correlation of GPS Coarse Acquisition code” ICCNT 2014, pp.178-181.
- C. Nadeu , J. Pascual and J. Hernando “pitch determination using the cepstrum Of the one-sided autocorrelation sequence” IEEE 1991, pp. 3677-3680.
- Y. Hu , M. Bhatnagar and P. C. Loizou, “A cross-correlation technique for enhancing speech corrupted with correlated noise” IEEE 2001, pp. 673-676.
- V. Grenier, K. Bry, J. L. Roux, M. Sulpis, “Autoregressive models for noisy speech signals” IEEE 1981, pp. 1093-1096.
- J. Benesty, J. Chen, and Y. (Arden) Huang, “On the Importance of the Pearson Correlation Coefficient in Noise Reduction” IEEE Trans. on Audio, Speech, and Language Process., vol. 16, no. 4, May 2008, pp. 757-765.
- T. Abe, M. Matsumoto, S. Hashimoto, “Parameter optimization in ε-filter for acoustical signal based on correlation coefficient” IEEE 2009, pp. 1417-1420.
- R. C. Hendriks and T. Gerkmann, “Noise Correlation Matrix Estimation for Multi-Microphone Speech Enhancement’ IEEE Trans. on Audio, Speech, and Language Process., vol. 20, no. 1, Jan. 2012, pp. 223-233.
- A. Schasse and R. Martin, “Online inter-frame correlation estimation methods for speech Enhancement in frequency subbands” IEEE, ICASSP 2013, pp. 7482-7486.
- A. Schasse and R. Martin, “Estimation of Subband Speech Correlations for Noise Reduction via MVDR Processing” IEEE/ACM Trans. on Audio, Speech, and Language Processing, vol. 22, no. 9, Sept., 2014, 1355-1365.
- H. Momeni, H. R. Abutalebi and E.A. P. Habets, “conditional mmse-based single-channel speech enhancement using inter-frame and inter-band correlations” IEEE, ICASSP 2016, pp. 5215-5219.
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