SIMULATION OF MODIFIED HYBRID NOISE REDUCTION ALGORITHM TO ENHANCE THE SPEECH QUALITY

A. Waqas, T. Muhammad, H. Jamal

Abstract


Speech is the most essential method of correspondence of humankind. Cell telephony, portable hearing assistants and, hands free are specific provisions in this respect. The performance of these communication devices could be affected because of distortions which might augment them. There are two essential sorts of distortions that might be recognized, specifically: convolutive and additive noises. These mutilations contaminate the clean speech and make it unsatisfactory to human audiences' i.e. perceptual value and intelligibility of speech signal diminishes. The objective of speech upgrade systems is to enhance the quality and understandability of speech to make it more satisfactory to audiences. This paper recommends a modified hybrid approach for single channel devices to process the noisy signals considering only the effect of background noises. It is a mixture of pre-processing relative spectral amplitude (RASTA) filter, which is approximated by a straightforward 4th order band-pass filter, and conventional minimum mean square error short time spectral amplitude (MMSE-STSA85) estimator. To analyze the performance of the algorithm an objective parameter called Perceptual estimation of speech quality (PESQ) is measured. The results show that the modified algorithm performs well to remove the background noises. SIMULINK implementation is also performed and its profile report has been generated to observe the execution time.

Full Text:

PDF

References


B. Gold and N. Morgan, Speech and Audio

Signal Processing, John Wiley & Sons, Inc.

(2000) Vii, 3, 4, 15.

C. H. You, S. N. Koh and S. Rahardja,

Adaptive β-order MMSE Speech

Enhancement Application for Mobile

Communication in a Car Environment, in

Proc. 4th Int. Conf. on Information,

Communications and Signal Processing, 4

th

Pacific Rim Conf. on Multimedia (2003) pp.

–1632.

T. Yamada, M. Kumakura and N. Kitawaki,

IEEE Trans. Audio, Speech, Language

Processing 14, Nov. (2006) 2006.

J. Benesty, S. Makino and J. Cheng, Speech

Enhancement, Springer Series of Signals

and Communication Technology (2005).

F. Toledo, P. C. Loizou and A. Lobo,

Subspace and Envelope Subtraction

Algorithms for Noise Reduction in Cochlear

Implants, IEEE Annual International

Conference of Engineering Medicine and

Biology Society (2003) pp. 213-216.

S. F. Boll, IEEE Trans. on Acoustic, Speech

and Signal Processing, ASSP-27, April

(1979) 113.

Y. Ephraim and D. Malah, IEEE Trans. on

Acoustics, Speech and Signal Processing,

ASSP-32 (1984) 1109.

Y. Ephraim and D. Malah, IEEE Trans. on

Acoustics, Speech and Signal Processing,

ASSP-33 (1985) 443.

C. Breithaupt and R. Martin, MMSE

Estimation of Magnitude-squared dft

Coefficients with Super Gaussian Priors in

Proc. IEEE Int. Conf. Acoust, Speech, Signal

Processing (2003) pp. 848-851.

H. Hermanskey and N. Morgan, IEEE Trans.

on Acoustics, Speech and Signal Processing

(1994) 578.

S.K. Shah, J.H. Shah and N.N.Parmar,

Evaluation of RASTA Approach with Modified

Parameters for Speech Enhancement in

Communication Systems, Proc. IEEE

Symposium on Computers and Informatics

(March 2011) pp. 159-162.

J. Shah and S. Shah, International Journal of

Computer Science 9, Issue 4, No 2 (2012)

S.K. Shah and J.H. Shah, Real Time and

Embedded Implementation of Hybrid

Algorithm for Speech Enhancement,

Information and Communication Technologies (WICT), Mumbai, India (2011) pp. 341-

D. Burshtein and S. Gannot, IEEE Trans.

Speech, Audio Process 10 (2002) 341.

The NOIZEUS database (2009) available at:

http://www.utdallas.edu/~loizou/speech/noize

A. Hu and P. Loizou, Subjective

Comparisons of Speech Enhancement

Algorithms, Proc. IEEE International

conference on Acoustics, Speech and Signal

Processing (May 2006).


Refbacks

  • There are currently no refbacks.