# 臺灣博碩士論文加值系統

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 本文提出一個以快速定位法為基礎的碼簿快速搜尋演算法，並採用學習與取捨的觀點實行此演算法。在向量空間中透過對各維度進行二元搜尋去定位出一個對應的搜尋子空間，並透過全域搜尋法或部分距離去除法在子空間中尋找最佳碼字，是為二元搜尋空間演算法。其中對於取捨的觀點來說，以些微向量量化品質的損失換取計算量大量的節省率；而就學習方面來說，以學習的方式並以全域搜尋演算法當作推論函數作為學習的對象去建立二元搜尋空間演算法的碼簿。此演算法應用在G.729語音編碼的線譜對二階合成向量編碼器上，其編碼器分別包含一個128碼字與兩個32碼字的小型碼簿。同時，介於語音訊號在訊框間的高相關性經過移動平均濾波器後，也隨之被消除。在線譜對編碼器上這些問題對於發展有效的快速搜尋演算法是一大挑戰。在實驗數據方面，動態交集三角不等式去除法、樹狀結構向量量化、二元搜尋空間向量量化等方法的計算量節省率分別為61.72%、88.63%與92.36%，而量化準確率分別為100%、26.07%與99.22%，以上數據驗證了二元搜尋空間向量量化優秀的效能。此外，二元搜尋空間向量量化與三角不等式去除法不同，在於它並不需要依賴訊號的高相關性來降低計算量，因此很適合使用在G.729語音編碼的線譜對向量編碼器上。
 This dissertation proposes a fast search algorithm for vector quantization (VQ) based on a fast locating method, and uses learning and trade-off analysis to implement this algorithm. The proposed algorithm is a binary search space VQ (BSS-VQ) that determines a search subspace by binary search in each dimension, and the full search VQ (FSVQ) or partial distance elimination (PDE) is subsequently used to obtain the best-matched codeword. In trade-off analysis, a slight loss occurred in quantization quality; however, a substantial computational saving was achieved. In learning analysis, the BSS was built by the learning process, which uses full search VQ (FSVQ) as an inferred function. The BSS-VQ algorithm is applied to the line spectral pairs (LSP) encoder of the G.729 standard, which is a two-stage VQ encoder with a codebook size of 128 and two small codebook sizes of 32. In addition, a moving average (MA) filters the LSP parameter beforehand, and the high correlation characteristics are degraded between consecutive speech frames. These factors present a challenge for developing fast and efficient search algorithms for VQ. In the experiment, the computational savings of DITIE, TSVQ, and BSS-VQ are 61.72%, 88.63%, and 92.36%, respectively, and the quantization accuracy of DITIE, TSVQ, and BSS-VQ are 100%, 26.07%, and 99.22%, respectively, which confirmed the excellent performance of the BSS-VQ algorithm. Moreover, unlike the TIE method, the BSS-VQ algorithm does not depend on the high correlation characteristics of signals to reduce the amount of computation; thus, it is suitable for the LSP encoder of the G.729 standard.
 中文摘要 iAbstract ii誌謝 iiiContents ivList of Tables viList of Figures viiChapter 1 INTRODUCTION 11.1 Background 11.2 Motivation 11.3 Organization of Dissertation 2Chapter 2 RELATED WORKS 32.1 Vector Quantization 32.1.1 Concept of vector quantization 32.1.2 Optimal quantization 52.2 Full Search Algorithm 92.3 Tree-Based Vector Quantization 102.3.1 Tree-structured vector quantization 102.3.2 Quasi-binary search algorithm 112.4 Fast Locating Based Algorithm 132.4.1 Double test of principal components algorithm 132.4.2 Planar Voronoi diagram search algorithm 152.5 Inequality-Based Algorithm 172.5.1 Partial distance elimination 172.5.2 Triangular inequality elimination 182.5.3 Dynamic and intersection TIE algorithm 192.6 G.729 Speech Codec 202.6.1 Encoder for G.729 codec 212.6.2 Decoder for G.729 codec 232.6.3 Quantization of LSP coefficients 242.6.3.1 Linear prediction analysis 242.6.3.2 Quantization of LSF coefficients 25Chapter 3 THE BINARY SEARCH SPACE VQ ALGORITHM 273.1 Concept of BSS-VQ 283.1.1 Preliminary concepts 283.1.2 BSS-VQ prototype 293.1.3 Description of BSS-VQ 313.2 Optimal Splitting Point 333.3 Learning Aspect of BSS-VQ 363.4 Trade-Off Analysis of BSS-VQ 393.5 Algorithm of BSS-VQ 43Chapter 4 EXPERIMENTAL RESULTS 454.1 BSS-VQ Training Process 454.1.1 Learning analysis for BSS-VQ 474.1.2 Trade-off analysis for BSS-VQ 534.2 BSS-VQ Encoding Process 58Chapter 5 CONCLUSIONS AND FUTURE WORKS 655.1 Conclusions 655.2 Future Works 65REFERENCE 67Publication List 73
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 1 對基週軌跡做向量量化之線性預估語音編碼 2 G.729之LSP向量量化計算複雜度化簡 3 AMR-WB語音編碼器之ISF向量量化編碼的搜尋效能改善研究 4 線頻譜頻率向量量化器之研究 5 可變位元率之CELP語音編碼器 6 差值向量量化法作語音編碼之探討 7 利用樹狀向量量化法作語音編碼之研究 8 簡化低位元率語音編碼技術之研究

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