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Autoregressive Hidden Markov Model based Speech Enhancement using Sparsity

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
A. Gayathri, G. Chenchamma, K. V. V. Kumar
10.5120/ijca2017912640

A Gayathri, G Chenchamma and K V V Kumar. Autoregressive Hidden Markov Model based Speech Enhancement using Sparsity. International Journal of Computer Applications 157(2):29-39, January 2017. BibTeX

@article{10.5120/ijca2017912640,
	author = {A. Gayathri and G. Chenchamma and K. V. V. Kumar},
	title = {Autoregressive Hidden Markov Model based Speech Enhancement using Sparsity},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2017},
	volume = {157},
	number = {2},
	month = {Jan},
	year = {2017},
	issn = {0975-8887},
	pages = {29-39},
	numpages = {11},
	url = {http://www.ijcaonline.org/archives/volume157/number2/26807-2017912640},
	doi = {10.5120/ijca2017912640},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Speech enhancement is required to enhance the quality of speech corrupted by the background noise and can be used in many applications such as hearing aids, mobile communication etc. In this paper a speech enhancement method is presented in which first Autoregressive (AR) model is applied for the noisy speech signal to find the speech parameters and then Hidden Markov model is applied to model those parameters. Later, the sparsity is encouraged into the model by adding the regularization parameter. The objective results for the proposed method and Wiener filter are compared. Speech quality in non-stationary noise conditions is observed through listening. The average log-likelihood score is obtained for different noises and observed that the performance is improved compared to the reference methods.

References

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  2. Lawrence R. Rabiner and Ronald W. Schafer, Introduction to Digital Speech Processing.
  3. Thomas F. Quatieri, Discrete-Time Speech Processing, Principles and Practice.
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  9. Feng Deng, Changchun Bao, and W. Bastiaan Kleijn, “Sparse HMM-based Speech Enhancement method for Stationary and Non-Stationary Noise Environments,” in proc. IEEE International conf. on Acoustics, Speech and signal Processing (ICASSP), 2015.
  10. D. Y. Zhao and W. B. Kleijn, “HMM-based gain modeling for enhancement of speech in noise,” IEEE Trans. Audio, Speech, Lang. Process., Vol. 15, no. 3, Mar. 2007.

Keywords

Speech enhancement, non-stationary noise, sparse autoregressive hidden markov model (SARHMM).