本文關(guān)鍵詞： 壓縮感知 語音信號 稀疏表示 循環(huán)觀測 小波樹模型　出處：《江南大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：壓縮感知理論是近年來信號處理領(lǐng)域的研究熱點，它能夠突破奈奎斯特采樣定律的限制，實現(xiàn)一種全新的邊壓縮邊采樣的采樣方式。而信號的稀疏表示是壓縮感知理論中的重要部分，能否得到原始信號更稀疏的表示直接影響到壓縮感知對信號觀測值的恢復(fù)效果。本文以漢語語音信號為研究對象，分別對DCT域、線性預(yù)測分析的殘差域和小波樹模型三種稀疏表示方法進行研究，將其應(yīng)用于語音壓縮感知框架中，改善重構(gòu)語音的質(zhì)量，具體的研究工作如下： 1.研究了基于DCT域稀疏預(yù)處理的語音壓縮感知方法。針對語音信號在DCT域的近似稀疏性導(dǎo)致重構(gòu)誤差較大的問題，提出了基于稀疏度和基于閾值兩種稀疏預(yù)處理的方法提高變換域的稀疏性，預(yù)處理后的信號犧牲了部分精度但得到了絕對的稀疏性，對預(yù)處理后的信號進行壓縮感知觀測，仿真實驗驗證了預(yù)處理方法的有效性，重構(gòu)語音的質(zhì)量得到了提高。 2.研究了基于循環(huán)觀測改進的線性預(yù)測語音壓縮感知方法。針對語音信號在線性預(yù)測殘差域的稀疏表示時，需要信號的線性預(yù)測系數(shù)來構(gòu)造稀疏變換矩陣，從而增加了預(yù)測系數(shù)傳輸數(shù)據(jù)量的問題，引入循環(huán)矩陣提出將線性預(yù)測系數(shù)存入對角陣向量中構(gòu)造循環(huán)矩陣，由此得到循環(huán)觀測矩陣再對語音信號觀測，同時提取該循環(huán)矩陣中的線性預(yù)測系數(shù)構(gòu)造殘差域稀疏變換矩陣，從而間接地減少線性預(yù)測系數(shù)的傳輸，仿真實驗表明預(yù)測系數(shù)循環(huán)觀測矩陣有穩(wěn)定的重構(gòu)性能，線性預(yù)測壓縮感知方法有更好的重構(gòu)效果，改進方法減少的數(shù)據(jù)量比例達到2.4%以上。 3.研究了基于小波樹稀疏性適應(yīng)觀測的語音壓縮感知方法。針對語音信號在小波樹中節(jié)點數(shù)目與觀測數(shù)目不匹配的問題，根據(jù)小波樹節(jié)點數(shù)修改了小波樹模型重構(gòu)算法中的初始支撐集，在大量實驗的基礎(chǔ)上得出固定觀測數(shù)下能夠最佳重構(gòu)的小波樹節(jié)點數(shù)，對于語音在小波樹中的稀疏性好的幀分配較多的觀測數(shù)目，較差的分配較少的觀測數(shù)，然后根據(jù)觀測數(shù)目調(diào)整小波樹的節(jié)點個數(shù)。仿真實驗結(jié)果表明，小波樹模型能保證較好的稀疏性，對不同稀疏性的語音幀采用不同觀測數(shù)，并選取最佳的小波樹節(jié)點數(shù)，平均重構(gòu)信噪比得到一定的提高，最后與前兩章方法比較了重構(gòu)語音質(zhì)量和重構(gòu)耗時，在兩者間得到一個較好的平衡。
[Abstract]:The compressed sensing theory of signal processing in recent years is a hot research field, it is able to break the laws of the Nyquist sampling limit, to achieve a new edge edge sampling method. The compression and sparse representation of signals is an important part in the theory of compressed sensing, can get a more sparse representation of the original signal directly affects the perception of compression the signal observations recovery. This paper takes Chinese speech signal as the research object, the DCT domain of linear prediction residual domain and wavelet tree model three sparse representation method is studied and applied to speech compressed sensing framework, improve the quality of the reconstructed speech, the main research is as follows:
1. the voice DCT domain sparsity pretreatment method based on compressed sensing. According to the approximate sparsity of speech signal in the lead to greater reconstruction error in DCT domain, we propose a sparse degree and threshold two methods based on sparse pretreatment to improve sparsity based on transform domain signal preprocessing after sacrifice some accuracy but the sparsity of the absolute, the preprocessed signal is compressed sensing, the simulation results verify the validity of pretreatment method, improves the quality of reconstructed speech.
2. of the linear circulation observation improved prediction method based on perceptual speech compression for sparse speech signal in the domain of linear prediction residual representation, signal of the linear prediction coefficients to construct the sparse transform matrix, thus increasing the prediction coefficient of transmission data, introducing a cyclic matrix, put forward linear prediction coefficients in constructing cycle matrix array vector, the observation matrix of speech signal cycle observation, the simultaneous extraction of the linear prediction coefficients to construct circular matrix residual domain sparse transform matrix, and thus indirectly reduce the transmission coefficient of linear prediction, simulation results show that the prediction coefficient of cyclic observation matrix reconstruction of stable performance, linear prediction method with compressed sensing reconstruction effect better, the amount of data to improve the methods to reduce the proportion of more than 2.4%.
3. research on wavelet tree sparse adaptive observations of speech compression method based on perception. For the voice signal in the wavelet tree node number and the number of observations does not match the problem, according to the number of tree nodes to modify the initial wavelet wavelet tree model reconstruction algorithm of the support set, based on a large number of experiments that the number of wavelet tree node optimal reconstruction the number of observations can be fixed, the number of observations for speech in the wavelet tree sparse good frame allocation more, less the number of observations is allocated according to the number of nodes, and then adjust the number of observed wavelet tree. Simulation results show that the wavelet tree model can guarantee the sparsity of speech frame is good, different the sparsity of the observation with different number, and select the number of wavelet tree node is the best, the average reconstruction signal-to-noise ratio can be improved, and finally the first two chapters compared with the method of reconstruction of speech quality and weight It takes time to get a better balance between the two.

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

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