語音信號的盲分離技術(shù)研究及應(yīng)用
發(fā)布時(shí)間：2018-09-05 14:06
【摘要】：語音信號的盲分離指的是在語音源信號的信息未知,同時(shí)也不知道混合系統(tǒng)的情況下,只能依據(jù)觀測到的語音信號來估計(jì)源信號的過程。主要用于現(xiàn)代的通信領(lǐng)域。隨著人類步入信息社會的步伐加快,越來越多的領(lǐng)域需要語音信號盲分離。語音信號盲分離研究主要在時(shí)域和頻域上展開。時(shí)域內(nèi)只能解決瞬時(shí)混合模型的語音信號,而有混響問題的卷積語音信號盲分離在頻域內(nèi)解決就容易很多。目前,語音盲分離領(lǐng)域已經(jīng)出現(xiàn)了很多算法,主要分為批處理算法和自適應(yīng)算法兩大類。批處理算法主要是聯(lián)合對角化算法,自適應(yīng)算法則以在線學(xué)習(xí)的梯度算法為主,有隨機(jī)梯度算法和自然梯度算法。大量的學(xué)者在這兩類算法上探索研究改進(jìn),Fast ICA算法應(yīng)運(yùn)而生,結(jié)合了批處理算法和自適應(yīng)算法的優(yōu)點(diǎn),對接收到的數(shù)據(jù)實(shí)施在線梯度算法,一次處理批量的數(shù)據(jù)并不斷更新迭代分離矩陣,有很好的收斂性能。跟其它的ICA算法一樣,FastICA算法也存在排序不確定性和幅度不確定性,本文對這部分難點(diǎn)做了大量仿真對比研究,最后實(shí)現(xiàn)了頻點(diǎn)對齊并消除了幅度不確定性。本文主要做了以下方面的研究:1.分析比較了ICA的四類獨(dú)立性判斷準(zhǔn)則,極大化似然度的判斷準(zhǔn)則、互信息最小化判斷準(zhǔn)則、信息最大化準(zhǔn)則以及極大化非高斯性準(zhǔn)則;研究分析了ICA中傳統(tǒng)的批處理算法及自適應(yīng)處理算法。2.深入討論FastICA算法,并根據(jù)目標(biāo)函數(shù)的不同,分別基于負(fù)熵、基于峭度以及基于似然度,分析比較三者的優(yōu)缺點(diǎn)。3.針對排序不確定性和幅度不確定性做了大量對比研究,最終通過基于功率比的相關(guān)系數(shù)和最小失真法消除了這兩種不確定性,并給出了仿真結(jié)果和對比分析。4.在語音盲分離的應(yīng)用方面,探究其在麥克風(fēng)陣列中的實(shí)際應(yīng)用,分別對實(shí)際的欠定語音信號和超定語音信號成功的實(shí)現(xiàn)盲分離。
[Abstract]:The blind separation of speech signals refers to the process of estimating the source signals only based on the observed speech signals when the information of the speech source signals is unknown and the mixed system is not known at the same time. Mainly used in modern communication field. With the acceleration of human step into the information society, more and more fields need blind separation of speech signals. Blind speech signal separation is mainly carried out in time domain and frequency domain. In time domain, only the speech signal of instantaneous mixing model can be solved, but the blind separation of convolutional speech signal with reverberation problem is much easier in frequency domain. At present, there are many algorithms in the field of speech blind separation, mainly divided into two categories: batch processing algorithm and adaptive algorithm. Batch algorithm is mainly a joint diagonalization algorithm, and adaptive algorithm is an online learning gradient algorithm, with random gradient algorithm and natural gradient algorithm. A large number of scholars have explored and studied the improved Fast ICA algorithm in these two kinds of algorithms. Combining the advantages of batch processing algorithm and adaptive algorithm, the online gradient algorithm is applied to the received data. It has good convergence performance by processing batch data and updating iterative separation matrix. Similar to other ICA algorithms, there are ordering uncertainties and amplitude uncertainties in FastICA algorithm. In this paper, a large number of simulation studies on these difficulties have been done. Finally, frequency alignment has been realized and amplitude uncertainty has been eliminated. This paper mainly does the following research: 1. Four kinds of independence criteria of ICA, maximum likelihood criterion, mutual information minimization criterion, information maximization criterion and maximization non-Gao Si criterion are analyzed and compared. The traditional batch processing algorithm and adaptive processing algorithm. 2. 2 in ICA are studied and analyzed. The FastICA algorithm is discussed in depth, and the advantages and disadvantages of the three algorithms are analyzed and compared based on negative entropy, kurtosis and likelihood respectively according to the difference of objective function. A large number of comparative studies have been done on sequencing uncertainty and amplitude uncertainty. Finally, the two uncertainties are eliminated by correlation coefficient and minimum distortion method based on power ratio, and the simulation results and comparative analysis are given. In the application of speech blind separation, the practical application in microphone array is explored, and the blind separation of actual underdetermined speech signal and overdetermined speech signal is realized successfully.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN912.3
本文編號：2224504
[Abstract]:The blind separation of speech signals refers to the process of estimating the source signals only based on the observed speech signals when the information of the speech source signals is unknown and the mixed system is not known at the same time. Mainly used in modern communication field. With the acceleration of human step into the information society, more and more fields need blind separation of speech signals. Blind speech signal separation is mainly carried out in time domain and frequency domain. In time domain, only the speech signal of instantaneous mixing model can be solved, but the blind separation of convolutional speech signal with reverberation problem is much easier in frequency domain. At present, there are many algorithms in the field of speech blind separation, mainly divided into two categories: batch processing algorithm and adaptive algorithm. Batch algorithm is mainly a joint diagonalization algorithm, and adaptive algorithm is an online learning gradient algorithm, with random gradient algorithm and natural gradient algorithm. A large number of scholars have explored and studied the improved Fast ICA algorithm in these two kinds of algorithms. Combining the advantages of batch processing algorithm and adaptive algorithm, the online gradient algorithm is applied to the received data. It has good convergence performance by processing batch data and updating iterative separation matrix. Similar to other ICA algorithms, there are ordering uncertainties and amplitude uncertainties in FastICA algorithm. In this paper, a large number of simulation studies on these difficulties have been done. Finally, frequency alignment has been realized and amplitude uncertainty has been eliminated. This paper mainly does the following research: 1. Four kinds of independence criteria of ICA, maximum likelihood criterion, mutual information minimization criterion, information maximization criterion and maximization non-Gao Si criterion are analyzed and compared. The traditional batch processing algorithm and adaptive processing algorithm. 2. 2 in ICA are studied and analyzed. The FastICA algorithm is discussed in depth, and the advantages and disadvantages of the three algorithms are analyzed and compared based on negative entropy, kurtosis and likelihood respectively according to the difference of objective function. A large number of comparative studies have been done on sequencing uncertainty and amplitude uncertainty. Finally, the two uncertainties are eliminated by correlation coefficient and minimum distortion method based on power ratio, and the simulation results and comparative analysis are given. In the application of speech blind separation, the practical application in microphone array is explored, and the blind separation of actual underdetermined speech signal and overdetermined speech signal is realized successfully.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN912.3
【參考文獻(xiàn)】
相關(guān)期刊論文 前1條
1 孟哲;基于小波變換的多尺度多閾值語音增強(qiáng)方法[J];武漢理工大學(xué)學(xué)報(bào)(交通科學(xué)與工程版);2001年02期
，本文編號：2224504
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