Identifier

etd-11092005-155756

Degree

Master of Science in Electrical Engineering (MSEE)

Department

Electrical and Computer Engineering

Document Type

Thesis

Abstract

In this thesis, a new robust noise spectral estimation algorithm is proposed for the purpose of single-microphone speech enhancement. This algorithm can generate the optimal noise spectral estimates in the Minimum Mean Square Error (MMSE) sense based on the speech statistics in the noisy environments. Compared to the well-adopted conventional noise spectral estimation method using the single-pole recursion, our proposed scheme is more reliable since the recursion coefficients are adaptable and optimal in the MMSE therein. We also propose a new accurate Resulting Signal-to-Noise Ratio (R-SNR) estimator as a quality measure to benchmark the existing noise spectral estimation techniques. This new R-SNR estimator can be applied to quantify not only the residual noise but also the speech distortion and therefore it can well serve as the overall speech quality measure after the noise suppression. We conduct the experiments to evaluate the performance of the noise suppression using our robust noise spectral estimation algorithm and compare it with those of two major existing noise spectral estimation methods. Through numerous simulations, we have shown that our noise suppression technique significantly outperforms the conventional methods in both stationary and nonstationary noise environments.

Date

2005

Document Availability at the Time of Submission

Secure the entire work for patent and/or proprietary purposes for a period of one year. Student has submitted appropriate documentation which states: During this period the copyright owner also agrees not to exercise her/his ownership rights, including public use in works, without prior authorization from LSU. At the end of the one year period, either we or LSU may request an automatic extension for one additional year. At the end of the one year secure period (or its extension, if such is requested), the work will be released for access worldwide.

Committee Chair

Hsiao-Chun Wu

DOI

10.31390/gradschool_theses.2467

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