【資訊科技】【2005.12】【含原始碼】基於幅度譜拉普拉斯運算元MMSE估計器的語音增強
多年來，人們研究了大量基於MMSE譜估計的語音增強演算法。雖然這些演算法中的一部分是基於拉普拉斯和伽瑪分佈的，但是沒有給出最優譜幅度估計。本文主要研究語音增強的幅度譜最優估計。本文給出了用拉普拉斯分佈模擬純語音DFT係數、用高斯分佈模擬噪聲DFT係數時，在MMSE意義下估計幅度譜的解析解。此外，我們推導了語音存在不確定性和拉普拉斯統計模型下的MMSE估計。結果表明，與傳統的基於高斯的MMSE估計相比，基於拉普拉斯的MMSE估計在增強後的語音中產生較少的殘留噪聲。總的來說，本研究表明DFT係數的分佈假設對增強語音的質量有顯著影響。
A number of speech enhancement algorithms based on MMSE spectrum estimators have been proposed over the years. Although some of these algorithms were developed based on Laplacian and Gamma distributions, no optimal spectral magnitude estimators were derived. This dissertation focuses on optimal estimators of the magnitude spectrum for speech enhancement. We present an analytical solution for estimating in the MMSE sense the magnitude spectrum when the clean speech DFT coefficients are modeled by a Laplacian distribution and the noise DFT coefficients are modeled by a Gaussian distribution. Furthermore, we derive the MMSE estimator under speech presence uncertainty and a Laplacian statistical model. Results indicated that the Laplacian-based MMSE estimator yielded less residual noise in the enhanced speech than the traditional Gaussian-based MMSE estimator. Overall, the present study demonstrates that the assumed distribution of the DFT coefficients can have a significant effect on the quality of the enhanced speech.
1 引言
2 歷史文獻回顧
3 基於拉普拉斯的MMSE STSA估計器
4 語音不確定條件下的幅度估計器
5 演算法實現與效能評估
6 總結
附錄A 用復拉普拉斯分佈的DFT係數推導譜幅度的概率密度函式PDF
附錄B 推導近似拉普拉斯MMSE估計器
附錄C 推導條件密度函式
附錄D 基於有限元法的數值積分技術
附錄E 目標效能評估方法
附錄F MATLAB原始碼
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