本文選題：盲源分離 + 線性混合��； 參考：《電子科技大學》2014年碩士論文

【摘要】：我們生活在聲音的世界里。在嘈雜的環(huán)境下,我們很難獲得理想的聲源,并且交談會變得不容易。因此從帶有噪聲的觀測信號中獲得我們想得到的目標源聲源信號,對于人與人或是人與機器的交流來說,都是相當重要的一件事情。因此盲聲源分離在人們的日常生活中是很重要的技術(shù)應(yīng)用。另外盲聲源分離技術(shù)在人和機器之間的聲源通信信道建構(gòu)也得到廣泛應(yīng)用。盲聲源分離(BSS)是一種僅僅使用每個通道觀測信號信息的估計源聲源信號的方法。我們事先并不需要知道源信號的信息,包括頻率特性、源信號空間位置或是源信號是怎樣混合的,就可以執(zhí)行這個估計過程。本文分別研究了聲源信號在超定情況下(m?n)卷積混合和欠定情況下(m?n)瞬時混合的盲信號分離問題。具體的研究工作如下:1.首先本文研究了系統(tǒng)是超定的情況下,即從m個卷積混合觀測信號中分離出n個源信號。具體步驟為:開始將時域觀測信號通過短時傅立葉變換(STFT)轉(zhuǎn)化到時頻域。然后利用FastICA分離算法在頻域分離觀測信號,最后通過頻點對換、幅度解混、時頻掩蔽和逆短時傅立葉變換一系列操作,我們最終就可以得到估計的源聲源信號。2.另外本文研究了系統(tǒng)是欠定的情況下,即從m個瞬時混合觀測信號中分離出n個源信號,本文的研究僅考慮了源信號傳播過程中幅度衰減和時間延遲的情況,而沒有考慮聲音混響的情況。分離過程中主要利用了觀測聲源信號在頻域的稀疏性。我們的分離算法是在2路觀測信號和3路源信號的實驗條件下完成的。具體過程可以分為三個階段:首先,在頻域通過勢函數(shù)聚類觀測信號的角度,將觀測信號按它們所屬的源進行劃分,這時可以估計出衰減矩陣。其次,對于每個劃分,通過補償一個可變時移,我們重新聚類觀測信號的角度,直到聚類再次出現(xiàn),每個可變時移就是時延矩陣的一列,這樣我們可以估計出了時延矩陣。最后,通過上面得到的衰減矩陣和時延矩陣,再加上信號頻譜系數(shù)的幅度是符合拉普拉斯分布這個假設(shè)。在混合方程的限定下,我們求得最小的信號幅度和。這實際上是一個二次錐規(guī)劃的問題。這樣我們就估計出了源信號。
[Abstract]:We live in the world of sound. In a noisy environment, it is difficult to get the ideal sound source, and the conversation will become difficult. Therefore, it is very important to get the source of the source of the target from the observed signal with the noise. It is very important for people and people to communicate with the machine. The separation of sound sources is a very important technical application in people's daily life. In addition, the blind source separation technology is also widely used in the sound source communication channel construction between human and machine. Blind source separation (BSS) is a method of estimating source signal only using the information of each channel observation signal. We do not need to know in advance. The information of the source signal, including frequency characteristics, the location of the source signal or how the source signal is mixed, can perform this estimation process. In this paper, the problem of blind signal separation for the instantaneous mixing of the sound source signals in the convolution mixing and the underdetermined case (M? N) under the overdetermined case is respectively studied. The specific research work is as follows: 1. first of all this paper When the system is overdetermined, the N source signal is separated from the M convolution mixed observation signal. The concrete steps are as follows: the time domain observation signal is converted to the time frequency domain through the short Fu Liye transform (STFT). Then the FastICA separation algorithm is used to separate the signal number in the frequency domain, and the frequency shift, the amplitude unmixing and the time frequency mask are finally obtained. In the case of a series of operations by Fu Liye transform and inverse short time, we can finally get the estimated source source signal.2.. In addition, we study that the system is under determined, that is, the N source signals are separated from the M instantaneous mixed observation signal. The study only considers the amplitude attenuation and the time delay in the source signal transmission. There is no consideration of sound reverberation. The separation process mainly uses the sparsity of the observed source signal in the frequency domain. Our separation algorithm is completed under the experimental conditions of the 2 path observation signal and the 3 source signal. The specific process can be divided into three stages: first, the observation in the frequency domain through the potential function clustering observation signal will be observed. The signal is divided according to the source they belong to, then the attenuation matrix can be estimated. Secondly, for each partition, by compensating a variable time shift, we re cluster the angle of the observation signal until the clustering appears again, and each variable time shift is a column of the time delay matrix, so we can estimate the time delay matrix. Finally, pass through The attenuation matrix and the time delay matrix above, and the amplitude of the signal spectrum coefficient are in accordance with the hypothesis of Laplasse distribution. Under the limit of the mixed equation, we get the minimum amplitude of the signal. This is actually a two time cone programming problem. In this way we estimate the source signal.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN911.7

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本文編號：1905108