CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Wiener Filter in Wavelet Domain for Mel-LPC based Noisy Speech Recognition

by M. Babul Islam, Md. Hamidul Islam, Md. Monsur Rahman
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 33
Year of Publication: 2018
Authors: M. Babul Islam, Md. Hamidul Islam, Md. Monsur Rahman
10.5120/ijca2018918221

M. Babul Islam, Md. Hamidul Islam, Md. Monsur Rahman . Wiener Filter in Wavelet Domain for Mel-LPC based Noisy Speech Recognition. International Journal of Computer Applications. 181, 33 ( Dec 2018), 1-4. DOI=10.5120/ijca2018918221

@article{ 10.5120/ijca2018918221,
author = { M. Babul Islam, Md. Hamidul Islam, Md. Monsur Rahman },
title = { Wiener Filter in Wavelet Domain for Mel-LPC based Noisy Speech Recognition },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2018 },
volume = { 181 },
number = { 33 },
month = { Dec },
year = { 2018 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number33/30199-2018918221/ },
doi = { 10.5120/ijca2018918221 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:07:58.198127+05:30
%A M. Babul Islam
%A Md. Hamidul Islam
%A Md. Monsur Rahman
%T Wiener Filter in Wavelet Domain for Mel-LPC based Noisy Speech Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 33
%P 1-4
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper deals with a wavelet domainWiener filter to estimate enhanced Mel-LPC spectra in presence of additive noises. In this implementation, Daubechies 4 (db4) wavelet function has been used as mother wavelet which enables a fast computation and decomposition using perfect reconstruction of filterbank. To implement the filter, noise is estimated from the initial 20 frames of input speech signal without applying any voice activity detection (VAD) system. In the proposed system, filtering is done in wavelet domain using Wiener gain. After filtering, inverse wavelet transform is applied to obtain enhanced time domain speech signal. Using this enhanced speech signal Mel-LP cepstral coefficients are calculated as speech feature. The proposed system is evaluated on Aurora-2 database and it has been found that the Wiener filter improves the overall word accuracy from 58.66 to 75.88% and the average Aurora-2 relative improvement has been found to be 42.50% for test set A.

References
  1. Gomez, R., et al. 2015. Optimized wavelet-domain filtering under noisy and reverberant conditions. APSIPA Transactions on Signal and Information Processing, 4.
  2. Islam, M. B., et al. 2007. Mel-Wiener filter for Mel-LPC based speech recognition. IEICE Transactions on Information and Systems, E90-D (6): 935-942.
  3. Ayat, S., et al. 2006. An improved wavelet-based speech enhancement by using speech signal features. Computers & Electrical Engineering, 32(6): 411-425.
  4. Cohen, I. 2003. Noise spectrum estimation in adverse environments: improved minima controlled recursive averaging. IEEE Transactions on Speech and Audio Processing, 11 (5): 466-475.
  5. Boll, S. F. 1979. Suppression of acoustic noise in speech using spectral subtraction. IEEE Transactions on Acoustics, Speech, and Signal Processing: 27(2): 113-120.
  6. Lockwood, P. and Boudy, J. 1992. Experiments with a Nonlinear Spectral Subtractor (NSS), Hidden Markov Models and the projection, for robust speech recognition in cars. Speech Communication: 11 (23): 215-228.
  7. Agarwal, A. and Cheng, Y. M. 1999. Two-Stage Mel- Warped Wiener Filter For Robust Speech Recognition. Proc. ASRU99: 67-70.
  8. Macho, D., et al. 2002. Evaluation of a noise-robust DSR front-end on Aurora databases. Proc. ICSLP: 17-20.
  9. Johnstone, I. M. and Silverman, B. W. 1997. Wavelet threshold estimators for data with correlated noise. Journal of the Royal Statistical Society: 59 (2): 319-351.
  10. Shao, Y. and Chang, C. H. 2005. A versatile speech enhancement system based on perceptual wavelet denoising. IEEE International Symposium on Circuits and Systems: 864-867.
  11. Bahoura, M. and Rouat, J. 2001.Wavelet speech enhancement based on the Teager energy operator. IEEE Signal Processing Letters: 8 (1): 10-12.
  12. Daubechies, I. 1990. The wavelet transform, time-frequency localization and signal analysis. IEEE Trans. on Information Theory: 36(5): 961-1005, 1990.
  13. Gomez, R. and Kawahara, T. 2010. Optimizing spectral subtraction and Wiener filtering for robust speech recognition in reverberant and noisy conditions. ICASSP2010.
  14. Oppenheim, A. V. and Johnson, D. H. 1972. Discrete representation of signals. IEEE Proc., 60(6): 681-691.
  15. Strube, H. W. 1980. Linear prediction on a warped frequency scale. J. Acoust. Soc. America, 68(4): 1071-1076.
  16. Matsumoto, H., et al. 1998. An efficient Mel- LPC analysis method for speech recognition. Proc. of ICSLP98: 1051- 1054.
  17. Hirsch, H. G. and Pearce, D. 2000. The AURORA experimental framework for the performance evaluation of speech recognition systems under noisy conditions. Proc. ISCA ITRW ASR2000: 181-188.
  18. Leonard, R. G. 1984. A database for speaker independent digit recognition. ICASSP84, 3: 42.11.1-42.11.4.
  19. ETSI standard document. 2000. Speech Processing, Transmission and Quality aspects (STQ); Distributed speech recognition; Front-end feature extraction algorithm; Compression algorithm. ETSI ES 201 108 v1.1.1 (2000-02).
Index Terms

Computer Science
Information Sciences

Keywords

Wiener filter Wavelet Transform Mel-LPC Noisy speech recognition Aurora-2 database

Powered by PhDFocusTM