IJE TRANSACTIONS C: Aspects Vol. 27, No. 9 (September 2014) 1367-1376    Article Under Proof

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Z. Mohammadpoory and J. Haddadnia
( Received: August 24, 2013 – Accepted: May 22, 2014 )

Abstract    In this paper an estimator for speech enhancement based on Laplacian Mixture Model has been proposed. The proposed method, estimates the complex DFT coefficients of clean speech from noisy speech using the MMSE estimator, when the clean speech DFT coefficients are supposed mixture of Laplacians and the DFT coefficients of noise are assumed zero-mean Gaussian distribution. Furthermore, the MMSE estimator under speech presence uncertainty and the Laplacian Mixture Model were derived. It is shown that the proposed estimator has better performance than three estimators based on single Gaussian and single Laplacian models. Also under speech presence uncertainty the results become better.


Keywords    EM algorithm, Gaussian noise, Laplacian Mixture Model, Minimum Statistic,MMSE estimator, speech presence uncertainty


چکیده    در این مقاله یک روش بهسازی گفتار آماری با فرض توزیع مخلوط لاپلاس برای گفتار، برای تخمین سیگنال گفتار تمیز (بدون نویز) از سیگنال گفتار نویزی ارائه شده است. در روش پیشنهادی، ضرایب تبدیل فوریه زمان کوتاه گسسته سیگنال گفتار با استفاده از تخمین گر کمترین میانگین مربعات خطا، بدست می آید. در این تخمین، فرض می شود که تابع چگالی احتمال ضرایب تبدیل فوریه سیگنال تمیز و نویز به ترتیب، مخلوط لاپلاس و گوسی با میانگین صفر می باشد. همچنین برا بهبود نتایج تخمین طیف با الحاق عدم فطعیت گفتار محاسبه شده است. نتایج حاصل از معیارهای SNRقطعه ای، LLR و PESQ نشان می دهد که روش پیشنهادی عملکرد بهتری نسبت به دو روش مبتنی بر توزیع گوسی و روش مبتنی بر توزیع لاپلاس دارد و با الحاق عدم قطعیت گفتار به تخمین گر، نتایج بهتر می شوند.



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