Speech enhancement based on hidden Markov model using sparse code shrinkage | ||
| Journal of AI and Data Mining | ||
| مقاله 9، دوره 4، شماره 2، مهر 2016، صفحه 213-218 اصل مقاله (822.78 K) | ||
| نوع مقاله: Research Note | ||
| شناسه دیجیتال (DOI): 10.5829/idosi.JAIDM.2016.04.02.09 | ||
| نویسندگان | ||
| E. Golrasan* ؛ H. Sameti | ||
| Department of Computer Engineering, Sharif University of Technology, Tehran, Iran. | ||
| چکیده | ||
| This paper presents a new hidden Markov model-based (HMM-based) speech enhancement framework based on the independent component analysis (ICA). We propose analytical procedures for training clean speech and noise models by the Baum re-estimation algorithm and present a Maximum a posterior (MAP) estimator based on Laplace-Gaussian (for clean speech and noise respectively) combination in the HMM framework, namely sparse code shrinkage-HMM (SCS-HMM). The proposed method on TIMIT database in the presence of three noise types at three SNR levels in terms of PESQ and SNR are evaluated and compared with Auto-Regressive HMM (AR-HMM) and speech enhancement based on HMM with discrete cosine transform (DCT) coefficients using Laplace and Gaussian distributions (LaGa-HMMDCT). The results confirm the superiority of SCS-HMM method in presence of non-stationary noises compared to LaGa-HMMDCT. The results of SCS-HMM method represent better performance of this method compared to AR-HMM in presence of white noise based on PESQ measure. | ||
| کلیدواژهها | ||
| Speech Signal Enhancement؛ HMM-based Speech Enhancement؛ Multivariate Laplace Distribution؛ Independent Component Analysis (ICA transform)؛ Sparse Code Shrinkage Enhancement Method | ||
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آمار تعداد مشاهده مقاله: 2,508 تعداد دریافت فایل اصل مقاله: 2,002 |
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