使用退火法之最小均方誤差噪音消除演算法最佳設計

Abstract

本論文將提出一項用在單聲道噪音減除的最佳語音改善演算法。此最佳處理程序是以從回歸模型取得之目標函數以及適合應付局部最佳解問題之退火演算法為基礎。最小均方誤差噪音消除演算法應用了時間遞迴平均法來估測噪音。在敏感度分析中可以發現最小均方誤差演算法的兩個參數有以下的特性:在不同的噪音環境下，一個參數變化相當劇烈，另一個則接近常數。另一應用線性預估編碼用來擷取人類語音相關部份如同前處理器的減噪演算法在本論文中也被提出。客觀與主觀測試被嘗試用來比較最佳化最小均方誤差時間回歸平均噪音消除演算法與一些傳統噪音消除演算法間的不同。訊噪比5dB的白噪音及汽車噪音被用來作為這些試驗的測試訊號。主觀測試的結果用變異數分析方法來做為分析的工具。進一步使用Tukey’s HSD 分析法來證明比起傳統慣用的噪音減除演算法新提出的方法在改善含有噪音的語音訊號上效果有明顯的進步並且提供更棒的音質。

This paper proposes an optimized speech enhancement algorithm aimed at single-channel noise reduction (NR). The optimization process is based on an objective function obtained in a regression model and the simulated annealing (SA) algorithm that is well suited for problems with many local optima. The NR algorithm, minimum mean-square error noise reduction (MMSE-NR) algorithm, employs a time-recursive averaging (TRA) method for noise estimation. It was found in a sensitivity analysis that one of the two optimal parameters remains relatively constant, while the other parameter varies drastically in different noise scenarios. Another NR algorithm proposed in the paper employs linear prediction coding (LPC) as a preprocessor for extracting the correlated portion of human speech. Objective and subjective tests were undertaken to compare the optimized MMSE-TRA-NR algorithm with several conventional NR algorithms. White noise and car noise at signal-to-noise ratio (SNR) 5 dB are used in these tests. The results of subjective test were processed by using analysis of variance (ANOVA) to justify the statistic significance. A post-hoc test (Tukey’s HSD) was conducted to assess the statistical difference between the NR algorithms. As compared to conventional algorithms, the optimized MMSE-TRA-NR algorithm proved effective in enhancing noise-corrupted speech signals, without compromising the timbral quality.