本文選題：語音增強 + 小波閾值�。� 參考：《深圳大學》2017年碩士論文

【摘要】：隨著計算機與數字信號處理技術的不斷發(fā)展,語音信號處理技術也不斷完善與成熟,并以視頻會議、手機、網絡視訊等各種形式廣泛應用于現(xiàn)實生活里面。但是各種各樣的噪聲會干擾語音通訊設備的通訊效果,嚴重妨礙了人們正常的交流與工作。所以,怎樣有效降低噪聲帶來的問題,提高有用語音信號質量,引起了海內外許多專家研究者們的興趣。目前也出現(xiàn)了較多的語音增強算法,一些在輸入噪聲較低情況下增強效果較好,高噪聲強度輸入下,增強效果不理想。因此,本文在針對各種信噪比輸入條件下進行研究。主要做了如下工作或改進:1:分析了小波去噪理論知識,主要深入研究小波閾值函數降噪算法,其目的是保留帶噪語音信號中有用語音小波系數,抑制噪聲系數。對一些小波閾值函數存在不連續(xù)、不同分解層數閾值恒定以及會產生恒定誤差等缺點。提出改進的帶調整參數連續(xù)小波閾值函數,并采用粒子群尋優(yōu)法尋找改進閾值函數在背景噪聲中的最優(yōu)值,最后將改進的函數與貝葉斯閾值方法相結合,進行小波重構處理后,得到處理后的語音信號系數。將提出的閾值函數與其他已存閾值函數去噪效果對比,仿真實驗結果表明在語音輸出信噪比、降低有用信號失真及抑制背景噪聲等方面有一定提高。2:改進型閾值函數的小波去噪算法在輸入噪聲強度不高的情況下降噪效果明顯,但是在低信噪比下還是殘留一些雜聲,因此,為進一步提高增強語音去噪算法效果,將帶噪語音信號經過改進型閾值函數小波去噪后作為先驗信息,再結合卡爾曼濾波算法,得到最終的增強信號。利用MATLAB平臺進行實驗,其結果表明該結合法能在高噪聲輸入條件下取得更好的增強效果。3:語音信號采集與顯示界面的編寫,在PC Windows操作系統(tǒng)上,采用VC++6.0軟件編寫的MFC界面,通過對USB聲卡和Windows所提供的音頻Wave類中函數完成對聲卡中音頻編程,實現(xiàn)語音采集界面。調用Windows所給的繪圖API函數,實現(xiàn)波形數據顯示功能。
[Abstract]:With the development of computer and digital signal processing technology, voice signal processing technology has been improved and matured, and has been widely used in real life in various forms such as video conference, mobile phone, network video. However, all kinds of noise will interfere with the communication effect of voice communication equipment, which seriously hinders people's normal communication and work. Therefore, how to effectively reduce the problems caused by noise and improve the quality of useful speech signal has attracted the interest of many experts and researchers at home and abroad. At present, there are many speech enhancement algorithms, some of which have better enhancement effect when the input noise is low, but the enhancement effect is not ideal under the high noise intensity input. Therefore, this paper studies on various SNR input conditions. The main work of this paper is as follows: firstly, the wavelet denoising theory is analyzed, and the wavelet threshold function denoising algorithm is studied in depth. The purpose of the algorithm is to retain the useful speech wavelet coefficients and suppress the noise coefficients in noisy speech signals. For some wavelet threshold functions there are some disadvantages such as discontinuity constant threshold of different decomposition layers and constant error. An improved continuous wavelet threshold function with adjusted parameters is proposed, and the particle swarm optimization method is used to find the optimal value of the improved threshold function in background noise. Finally, the improved function is combined with Bayesian threshold method to reconstruct the wavelet. The speech signal coefficients after processing are obtained. The proposed threshold function is compared with that of other existing threshold functions. The simulation results show that the signal-to-noise ratio (SNR) of the speech output is obtained. In some aspects, such as reducing useful signal distortion and suppressing background noise, the wavelet denoising algorithm with improved threshold function can reduce noise obviously when the input noise intensity is not high, but it still has some residual noise under low SNR. Therefore, in order to further improve the effect of enhanced speech denoising algorithm, the noisy speech signal is de-noised by the improved threshold function wavelet as the prior information, and then the final enhanced signal is obtained by combining the Kalman filter algorithm. The results show that the combined method can achieve better enhancement effect under the condition of high noise input. The interface of voice signal acquisition and display is compiled. On PC Windows operating system, MFC interface is written with VC 6.0 software. Through the function of USB sound card and the audio wave class provided by Windows, the audio program of the sound card is completed, and the interface of voice acquisition is realized. The drawing API function given by Windows is called to realize the display function of waveform data.
【學位授予單位】：深圳大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN912.35

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本文編號：2033503