基於資訊最大化及時頻聚類之盲訊號源分離超大型積體電路架構設計

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姓名

陳映全(YING-CHUAN CHEN) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

基於資訊最大化及時頻聚類之盲訊號源分離超大型積體電路架構設計
(VLSI Architecture Design for Blind Source Separation based on Infomax and Time-frequency Masking)

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摘要(中)

盲訊號分離的研究可以分為即時混合(Instantaneous Mixture)和旋積混合(Convolutive Mixtures)，在即時混合的研究上，已有不錯的成果，因為解混合矩陣在訓練係數過程中需要很大的運算量，所以將這個部分實現在超大型積體電路(Very-large-scale integration , VLSI)是一個不錯的選擇，本論文想在解旋積混合的硬體做一些改進，希望可以加快處理速度，應付未來應用上之需要。我們主要採取的演算法有兩種並將其實現為超大型積體電路，其中第一種為Infomax演算法，我們使用Torkkola所提出的架構來實現旋積盲訊號源分離，Torkkola的學習規則近似於最小均方誤差演算法，所以我們利用近似於最小均方誤差適應性濾波器的延遲最小均方誤差適應性濾波器來做修改應用到旋積盲訊號源分離中；而第二種為時頻聚類之盲訊號源分離，主要是將訊號做特徵擷取後再經過聚類演算法來達到訊號分離的效果，在這我們提出了特徵節取和K-means的硬體架構。此外我們利用了壓縮感測來增強和重建其分離的訊號，最後也提出了壓縮感測中Orthogonal Matching Pursuit的硬體架構。

摘要(英)

Blind source separation (BSS) of independent sources from their convolutive mixtures is a problem in many real world applications. In this paper, we design two VLSI architectures for convolutive BSS (CBSS). The first is based on Infomax algorithm and the BSS structure proposed by Torkkola is utilized. As its learning rule is similar to least mean squares (LMS), we apply delayed LMS (DLMS) to BSS. The proposed architecture based on sharing multiplication improves adaptation delays and critical path. The second VLSI architecture is based on time-frequency masking based BSS. This method generates useful features for each time-frequency points and then clusters them to achieve signal separation. For this algorithm, we propose VLSI modules for feature generation and K-means and orthogonal matching pursuit.

關鍵字(中)

★ 適應性濾波器
★ 盲源分離
★ 資訊最大化
★ 壓縮感測

關鍵字(英)

★ Blind Source Separation
★ adaptive filter
★ Orthogonal Matching Pursuit
★ Infomax

論文目次

摘要...........................................................................................................................................ii
Abstract……………………………………………………………………………………....iii
圖目錄…………………………………………………………………………………………iv
表目錄 …………………………………………………………………………………….vi
章節目次 vii
第一章緒論 1
1.1 前言 1
1.2 研究動機與目的 1
1.3 論文架構 2
第二章盲訊號源分離簡介 4
2.1 簡介(Introduction) 4
2.2 混合模型(Mixing Model) 4
2.2.1旋積混合模型(Convolutive Mixtures Model) 4
2.2.2即時混合模型(Instantaneous Mixing Model) 5
2.2.3在頻率域上的旋積混合 6
2.3 Over and Under-determined 6
2.4 分離模型(Separation Model) 7
2.4.1 Feed-forward Structure 8
2.4.2 Feedback Structure 9
2.4.3 兩個輸入兩個輸出系統 10
2.5 分離原理 11
2.5.1 Independent Component Analysis(ICA) and BSS 11
2.6 盲訊號源分離(Blind source separation)硬體架構相關研究 12
第三章基於Infomax盲訊號源分離之VLSI架構 13
3.1 Information Theoretic 13
3.2延遲最小均方演算法(Delayed Least Mean Squares) 17
3.3延遲最小均方演算法之硬體架構 19
3.3.1 Proposed design for enhance DLMS architecture 21
3.5從Infomax解BSS之演算法到VLSI架構 26
第四章時頻聚類盲訊號源分離之VLSI架構 34
4.1 概觀時頻聚類盲訊號源分離 34
4.2 特徵參數選取與其VLSI架構 36
4.2.1 特徵參數選取演算法 36
4.2.2 特徵參數選的VLSI架構 37
4.3 K-Means分群演算法與其VLSI架構 38
4.3.1 可硬體式K-Means演算法 39
4.3.2 K-Means 硬體架構 40
4.4 壓縮感測與其VLSI架構 45
4.4.1壓縮感測演算法 45
4.4.2 OMP(Orthogonal Matching Pursuit)硬體架構 47
第五章實驗結果 61
5.1 基於infomax盲訊號源分離之VLSI架構 61
5.2 時頻聚類之盲訊號源分離之VLSI架構 65
第六章結論及未來研究方向 69
參考文獻 71

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指導教授

王家慶(JIA-CHING WANG)

審核日期

2012-8-7

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