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Please use this identifier to cite or link to this item: http://ir.lib.stu.edu.tw:80/ir/handle/310903100/1307

Title: 利用小波複合比較權重樹於G.726語音編碼器之研究
A study on modified G.726 codec using compound wavelet weight tree
Authors: 陳嘉祥
Chen-Chia_Hsiang
Contributors: 陳璽煌
Shi-Huang Chen
資訊工程學系
Keywords: 小波轉換;知覺小波封包轉換;演算法;權重樹;編碼器;資訊量;適應性;適應性門檻值;
Wavelet Transform;Perceptual Wavelet Packet;Adaptive Weighted Threshold;Entropy;after
Date: 2006
Issue Date: 2011-05-24 15:12:14 (UTC+8)
Publisher: 高雄市:[樹德科技大學資訊工程學系]
Abstract: 本論文提出一種「知覺小波封包能量權重樹」搭配「小波封包Entropy(熵)權重樹」演算法,可以提高現有的波形編碼器G.726之壓縮效率。
藉由本文提出之「知覺小波封包能量權重樹演算法」,將有聲段訊號分解成17個知覺小波封包轉換訊號段,接著保留17個訊號段中,能量較高之訊號段,此演算法能根據不同語音訊號,保留不同的有效資料段,做到適應性動態調整的效果;最後透過「小波封包Entropy(熵)權重樹」,保留有效資訊量較高之訊號段,此演算法能針對不同語音訊號所計算出之不同有效資訊量分布,達到動態調整之效果,各語音訊號所保留之有效訊號段皆有所不同。
經由研究結果顯示,本論文所提出之新型演算法不僅可以大幅提高壓縮率,並可依照不同的語音檔案特性動態調整壓縮率與解壓縮後之語音品質,同時還原後之語音訊號仍能保持波形編碼高語音品質之特性。
This paper proposes a new “perceptual wavelet packet energy weight tree” algorithm embedded with ”wavelet packet entropy weight tree” to improve the coding efficiency of waveform codec, e.g. ITU-T G.726.
By the use of the proposed algorithm, the input speech signal will be first decomposed into 17 subbands. Then the algorithm will dynamically remain the subbands with larger energy based on the speech characteristically. Finally, the proposed algorithm will apply wavelet packet entropy weight tree to these subbands remained from the prevision step and determ the entropy of each subband.
This process will reserve the subbands with large energy and higher entropy for the following G.726 encoder.
It follows from various experimental results, the proposed algorithm can increase the compression ratio of the original G.726 codec and keep the high quality speech signal after decoding.
Appears in Collections:[資訊工程系(所) ] 博碩士論文

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