本文選題：盲源分離 + 變分貝葉斯��； 參考：《蘭州交通大學(xué)》2017年碩士論文

【摘要】：盲源分離算法因為其在科學(xué)研究和工程應(yīng)用領(lǐng)域的廣闊發(fā)展前景而受到越來越多的研究人員的注意。近幾年盲源分離算法開始在信號處理領(lǐng)域得到廣泛應(yīng)用,且由于良好的處理結(jié)果而受到廣泛關(guān)注。但是傳統(tǒng)的獨(dú)立分量分析方法在對語音信號進(jìn)行分離的時候存在沒有考慮噪聲對混合系統(tǒng)的干擾,對觀測信號和已知的先驗信息沒有充分利用導(dǎo)致分離效果不理想,以及對語音信號的內(nèi)在結(jié)構(gòu)特征沒有充分考慮等不足。為了克服以上的不足,使語音分離更符合實際情況,提高其應(yīng)用價值,本文在研究中將變分貝葉斯獨(dú)立分量法引入有噪語音分離系統(tǒng)進(jìn)行分析,并針對語音信號內(nèi)在所包含的時間結(jié)構(gòu)特性,運(yùn)用自回歸模型對語音信號進(jìn)行建模,提出了基于AR模型的變分貝葉斯獨(dú)立分量分析算法。然后運(yùn)用仿真實驗和評價指標(biāo)分析來驗證了算法的效果。本文的主要內(nèi)容有如下幾個方面:首先,簡要的介紹了盲源分離的理論的相關(guān)知識,主要有相應(yīng)的原理和數(shù)學(xué)模型,獨(dú)立分量分析的幾種目標(biāo)函數(shù)和優(yōu)化算法,以及算法前的預(yù)處理方法,并對兩種經(jīng)典的傳統(tǒng)獨(dú)立分量分析算法進(jìn)行了推導(dǎo)和分析。其次,引入了變分貝葉斯獨(dú)立分量分析對含噪聲的語音混合系統(tǒng)進(jìn)行分離,從貝葉斯網(wǎng)絡(luò)和貝葉斯推論入手,充分利用了混合系統(tǒng)的先驗信息,為了解決混合系統(tǒng)后驗概率計算非常復(fù)雜的問題,運(yùn)用了變分近似的方法完成了整個變分貝葉斯獨(dú)立分量分析的原理推導(dǎo),并通過與上文兩種經(jīng)典的獨(dú)立分量分析的算法進(jìn)行仿真和評價指標(biāo)的對比,表明該算法結(jié)果更優(yōu)。最后,針對語音信號內(nèi)在的時間特性,在上文的基礎(chǔ)上提出了基于泛化自回歸模型的變分貝葉斯獨(dú)立分量分析算法。該算法的特點(diǎn)是把源信號具有的時間結(jié)構(gòu)和系統(tǒng)噪聲的情況納入一個框架進(jìn)行學(xué)習(xí),運(yùn)用泛化自回歸模型來近似地建模語音信號所具有的時間結(jié)構(gòu)并給出了完整的理論推導(dǎo)過程。最后用變分貝葉斯學(xué)習(xí)方法分離出含噪聲的語音信號。通過與標(biāo)準(zhǔn)的變分貝葉斯獨(dú)立分量分析的仿真對比證明了改進(jìn)后的算法的分離效果有了很大的提高。
[Abstract]:Blind source separation (BSS) algorithms have attracted more and more researchers' attention due to their broad development prospects in scientific research and engineering applications. In recent years, blind source separation algorithms have been widely used in the field of signal processing. However, the traditional independent component analysis (ICA) method does not take into account the interference of the noise to the hybrid system when the speech signal is separated, and the incomplete utilization of the observed signal and the known prior information leads to the unsatisfactory separation effect. And the internal structure of the speech signal is not fully considered and so on. In order to overcome the above shortcomings, make speech separation more in line with the actual situation and improve its application value, this paper introduces the variational Bayesian Independent component method into the noisy speech separation system for analysis. According to the time structure characteristic of speech signal, the autoregressive model is used to model the speech signal, and a variational Bayesian independent component analysis algorithm based on AR model is proposed. Then the simulation experiment and evaluation index analysis are used to verify the effectiveness of the algorithm. The main contents of this paper are as follows: firstly, the related knowledge of blind source separation theory is briefly introduced, including the corresponding principle and mathematical model, several objective functions and optimization algorithms of independent component analysis. Two classical independent component analysis (ICA) algorithms are derived and analyzed. Secondly, variational Bayesian Independent component Analysis (VICA) is introduced to separate the noisy speech mixing system. Based on the Bayesian network and Bayesian inference, the prior information of the hybrid system is fully utilized. In order to solve the complex problem of posteriori probability calculation of hybrid systems, the variational approximation method is used to complete the principle derivation of the whole variational Bayesian independent component analysis. The results of simulation and evaluation are compared with the two classical independent component analysis (ICA) algorithms, and the results show that the proposed algorithm is better. Finally, a variational Bayesian independent component analysis (ICA) algorithm based on generalized autoregressive model is proposed based on the inherent temporal characteristics of speech signals. The characteristic of the algorithm is that the time structure of the source signal and the noise of the system are incorporated into a framework for learning. The generalized autoregressive model is used to approximate the time structure of speech signal and the complete theoretical derivation process is given. Finally, the noisy speech signal is separated by variational Bayesian learning method. The comparison with the standard variational Bayesian Independent component Analysis (ICA) shows that the improved algorithm can improve the separation performance greatly.
【學(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN912.3

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