【摘要】：近十年來,壓縮感知理論(compressed sensing)成為信號處理方向的熱門研究方向,CS理論解決了傳統(tǒng)采樣機制中采樣率高的難題,可以大大減少資源的浪費,僅需少量采樣值即可在接收端精確或近似地重構(gòu)原始信號。語音信號具有稀疏性,而如果通過引入壓縮感知技術,將其和語音信號處理結(jié)合,這將會給語音信號處理領域帶來新的發(fā)展。本文的研究就是基于這個前提,針對在實際的應用中語音壓縮感知系統(tǒng)必然含有噪聲,主要考慮CS系統(tǒng)中稀疏表示和觀測矩陣的部分來研究消噪技術,以提升系統(tǒng)魯棒性。本學位論文的研究內(nèi)容和創(chuàng)新點如下:首先,詳細闡述了關于壓縮感知理論的研究背景知識,概括了壓縮感知理論發(fā)展的數(shù)十年來各種關鍵技術的研究現(xiàn)狀,總結(jié)性地介紹了語音壓縮感知技術的應用與發(fā)展,本團隊在前期的工作成果等。其次,從壓縮感知理論涉及的稀疏基、觀測矩陣和重構(gòu)算法三個核心技術方面來詳細地介紹。然后,重點對語音信號的特征進行研究,經(jīng)過一系列的仿真實驗,證實了將CS技術應用于語音信號處理中是可行的。最后,考察了含噪語音在壓縮感知系統(tǒng)中的性能,以及噪聲對CS系統(tǒng)各部分的影響。正是建立在這些研究的前提之上,本論文提出了一種基于FIST算法的改進K-SVD字典學習方法。通過將快速迭代收縮閾值算法引入字典訓練過程,提出了基于快速迭代收縮閾值算法的K-SVD字典學習算法。該算法首先用快速迭代收縮閾值算法來完成K-SVD字典學習算法的稀疏編碼階段,更新字典則使用K-SVD的經(jīng)典更新方法,稀疏編碼和字典更新兩步迭代學習得到新的字典。將其訓練出的字典對語音信號進行稀疏化,再觀測重構(gòu),并將此算法應用于語音信號的壓縮感知過程。結(jié)果表明本文算法比經(jīng)典的K-SVD算法字典訓練速度快、RMSE低。進一步考察算法的語音去噪能力,在白噪聲環(huán)境下并考察不同字典參數(shù)時的字典性能,實驗結(jié)果表明本文算法比經(jīng)典的K-SVD算法獲得更高的輸出信噪比,具有良好的去噪性能。最后,本文提出了一種設計最佳投影和獲得學習字典的聯(lián)合設計方法,以此來提升壓縮感知應用中的重構(gòu)和消噪性能�；趯σ粋�給定的字典存在封閉的表達形式的前提,對字典SVD分解,通過數(shù)學推導得到投影矩陣的表達式,此時投影矩陣和字典相乘是一個Parseval緊框架。設計得到的最佳投影矩陣可以通過字典得到。仿真結(jié)果顯示,與其他方法相比,本文提出的設計方法應用于語音信號有較好的消噪性能。
[Abstract]:In the past ten years, the compressed sensing theory (compressed sensing) has become a hot research direction in signal processing. CS theory solves the problem of high sampling rate in traditional sampling mechanism, and can greatly reduce the waste of resources. The original signal can be reconstructed accurately or approximately at the receiver with only a few sampling values. Speech signal is sparse, but if compression sensing technology is introduced and combined with speech signal processing, it will bring new development to the field of speech signal processing. The research of this paper is based on this premise. In order to improve the robustness of the CS system, the sparse representation and the observation matrix are considered in order to improve the robustness of the system. The research contents and innovations of this dissertation are as follows: firstly, the background knowledge of the theory of compressed perception is described in detail, and the research status of various key technologies in the development of the theory of compressed perception is summarized. This paper summarizes the application and development of speech compression perception technology, the team's previous work and so on. Secondly, the sparse basis, observation matrix and reconstruction algorithm of compressed sensing theory are introduced in detail. After a series of simulation experiments, it is proved that it is feasible to apply CS technology to speech signal processing. Finally, the performance of noisy speech in compression sensing system and the effect of noise on each part of CS system are investigated. Based on these researches, this paper proposes an improved K-SVD dictionary learning method based on FIST algorithm. By introducing the fast iterative shrinkage threshold algorithm into the dictionary training process, a K-SVD dictionary learning algorithm based on the fast iterative contraction threshold algorithm is proposed. The algorithm uses the fast iterative shrinkage threshold algorithm to complete the sparse coding phase of the K-SVD dictionary learning algorithm, and the update dictionary uses K-SVD 's classical updating method, sparse coding and dictionary updating two-step iterative learning to obtain the new dictionary. The dictionary is used to sparse the speech signal, and then the algorithm is applied to the process of speech signal compression and perception. The results show that the proposed algorithm is faster than the classical K-SVD algorithm in dictionary training speed and lower in RMSE. Furthermore, the speech denoising ability of the algorithm and the dictionary performance under white noise and different dictionary parameters are investigated. The experimental results show that the proposed algorithm has higher output SNR than the classical K-SVD algorithm. It has good denoising performance. Finally, a joint design method of optimal projection and learning dictionary is proposed to improve the performance of reconstruction and de-noising in compression sensing applications. Based on the premise that there is a closed representation for a given dictionary, the SVD decomposition of the dictionary is used to derive the expression of the projection matrix by mathematical derivation. In this case, the multiplying of the projection matrix and the dictionary is a Parseval compact frame. The optimal projection matrix can be obtained by dictionary. The simulation results show that compared with other methods, the proposed design method has better denoising performance.
【學位授予單位】：南京郵電大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN912.3

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