本文關(guān)鍵詞：基于壓縮感知的語音信號壓縮重構(gòu)算法研究　出處：《中北大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 壓縮感知 語音信號 自適應(yīng)算法

【摘要】：傳統(tǒng)香農(nóng)采樣定理要求采樣率必須高于信號最高頻率的兩倍，就可以較好恢復(fù)原信號，雖然實現(xiàn)了信號的采集、壓縮和恢復(fù)，但隨著采集數(shù)據(jù)和頻率的急劇增加，使得目前通信系統(tǒng)越來越難以承受。Candès等人提出的壓縮感知理論很好地解決了這個難題。壓縮感知將可稀疏的信號通過觀測從高階矩陣線性投影為低階，，信號的采集和壓縮在此過程同時進行，最后高概率精確地重建原始信號。壓縮感知跳出了傳統(tǒng)采樣的思維模式，所以必然會改變未來信號處理的方式。 首先，本文綜述了壓縮感知以及在語音信號處理領(lǐng)域的研究現(xiàn)狀，系統(tǒng)概述了壓縮感知數(shù)學(xué)模型，圍繞信號稀疏，設(shè)計觀測矩陣和選擇重構(gòu)算法三個關(guān)鍵技術(shù)進行分類比較，并且分析了設(shè)計觀測矩陣的約束條件，討論了壓縮感知與香農(nóng)采樣的區(qū)別和聯(lián)系。 其次，概述了傳統(tǒng)語音信號處理的主要過程和語音信號特征，因為語音具有良好的可壓縮性，所以壓縮感知理論可以實現(xiàn)語音信號的壓縮重構(gòu)。選擇DCT為稀疏基并驗證了在清濁音的稀疏性，最后進行實驗分析，選取一段采樣率為22.05K的語音信號，通過OMP算法和BP算法分別實現(xiàn)語音信號重構(gòu)，并對恢復(fù)的語音信號進行主觀和客觀評價分析，最后得出結(jié)論：（1）語音信號的壓縮比值和幀長對重構(gòu)信號的質(zhì)量都有影響；（2）BP算法重構(gòu)語音質(zhì)量比OMP算法的高，但恢復(fù)信號時間較長。 最后，結(jié)合普通壓縮感知中稀疏基、觀測矩陣和重構(gòu)算法的缺點引入自適應(yīng)算法，利用自適應(yīng)冗余字典KSVD算法、自適應(yīng)觀測矩陣和SAMP重構(gòu)算法，提出自適應(yīng)壓縮感知的概念，介紹了上述算法的具體實現(xiàn)步驟，并分別進行仿真對比，驗證了KSVD算法具有更好的稀疏性，根據(jù)每幀語音能量自適應(yīng)分配觀測個數(shù)，顯著提高了重構(gòu)語音的質(zhì)量，SAMP縮減了重構(gòu)信號所用時間，最后對自適應(yīng)壓縮感知進行仿真分析和主客觀評價，對比了普通壓縮感知重構(gòu)信號，自適應(yīng)壓縮感知具有重構(gòu)語音質(zhì)量顯著提高并且運行時間明顯減少等優(yōu)點，從而驗證了自適應(yīng)壓縮感知的可行性。
[Abstract]:Two times the traditional Shannon sampling theorem requires that the sampling rate must be higher than the highest frequency of the signal, you can restore the original signal is good, although the realization of signal acquisition, compression and recovery, but with a sharp increase in data acquisition and frequency, the communication system becomes more and more difficult to bear.Cand s proposed the theory of compressed sensing is very good to resolve this problem. The compressed sensing will be sparse signal by observing from the matrix of high order linear projection for low order, signal acquisition and compression in this process at the same time, the high probability of accurately reconstruct the original signal. Compressed sensing sampling out of the traditional mode of thinking, so will change the future way of signal processing.
First of all, this paper reviews the research status of compressed sensing and processing of speech signal in the field of system overview of the compressed sensing model, on the design of the observation matrix and sparse signal, select the reconstruction algorithm of three key techniques for classification and analysis of constraints, the design of the observation matrix, the relation and difference between the compressed sensing and Shannon sampling the discussion.
Secondly, summarizes the main process and characteristics of the traditional speech signal processing of speech signal, because the speech has good compressibility, so the theory of compressed sensing technology can reconstruct the speech signal compression. Select DCT for sparse matrix and verify the sparsity in voicing, and finally the experimental analysis, select a sampling rate of speech the 22.05K signal, the speech signal reconstruction respectively through the OMP algorithm and BP algorithm, and the voice signal recovery to analyze the subjective and objective evaluation, finally draws the conclusion: (1) effect of mass ratio and frame compression of speech signal length on signal reconstruction; (2) the BP algorithm of speech quality than OMP algorithm the high, but to restore the signal for long time.
Finally, combined with the ordinary compressed sparse basis perception, observation matrix and reconstruction algorithm of the shortcomings of the adaptive algorithm is introduced, using adaptive redundant dictionary KSVD algorithm, adaptive observation matrix and SAMP reconstruction algorithm, put forward the concept of adaptive compressed sensing, introduces the specific implementation steps of the algorithm, and the simulation results were verified, sparse KSVD algorithm better, according to each frame of speech energy adaptive allocation of the number of observations, significantly improve the quality of reconstructed speech, SAMP reduced the reconstructed signal with time, finally the adaptive CS was simulated and the subjective and objective evaluation, compared with the common compressed sensing signal reconstruction, adaptive compressed sensing has significantly improved the quality of reconstructed speech and operation less time etc, which verifies the feasibility of adaptive compressed sensing.

【學(xué)位授予單位】：中北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN912.3

【引證文獻】

相關(guān)碩士學(xué)位論文 前1條

1 黃晶晶;基于壓縮感知的多選擇正交匹配追蹤改進算法研究[D];安徽大學(xué);2015年

本文編號：1379723