結合時頻遮罩與壓縮感測之盲訊號源分離方法

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姓名

張淳皓(Chun-hao Zhang) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

結合時頻遮罩與壓縮感測之盲訊號源分離方法
(Blind Source Separation Using Time-Frequency Masking and Compressive Sensing)

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摘要(中)

為了解決旋積盲訊號源分離這個問題，本論文提出了一個結合時頻遮罩分離與壓縮感測新方法。首先我們先定義兩個特徵參數包括了level-ratio以及phase-difference，然後利用KNN Graph方式，去除資料中的離群樣本，並用K-Means演算法對每個頻帶非離群資料群聚。運用DOA方位角估測方法可以將每個頻帶的群聚中心，找出每個群聚中心角度後可以找出其他頻帶有相近方位角群聚類別視為同一來源訊號。將先前利用KNN Graph方式設定為離群資料的資料點重新分群後。對於每個來源的時頻遮罩已經可以藉由群聚結果計算出來。我們利用壓縮感測去估算那些時頻域上未知的資料，藉此增加每個分離訊號分離效果。我們運由KSVD演算法將時頻能量矩陣訓練出重建字典。

摘要(英)

To solve the convolutive blind source separation (BSS) problem, this thesis presents a new method which integrates time-frequency masking and compressive sensing (CS). We first define two features called level-ratio and phase-difference. Next, we eliminate outliers by KNN graph and use K-Means clustering to obtain the separated clusters in each frequency bin. A DOA detection method is then used to associate the cluster centroid with the corresponding source and this procedure is performed in all the frequency bands. The outliers eliminated by KNN graph are then reassigned to cluster centroids and time-frequency masking associated with each source can be designed. We use compress sensing (CS) to impute the unknown time-frequency points to enhance the quality of the separated sources. To build the atoms of the redundant dictionary for CS, frequency magnitude vectors obtained by short time Fourier Transform are trained by K-SVD algorithm to assure the sparseity of the dictionary.

關鍵字(中)

★ 時頻遮罩
★ 壓縮感測
★ 盲訊號源分離

關鍵字(英)

★ Blind Source Separation
★ Compressive Sensing
★ Time-Frequency Masking

論文目次

章節目次
中文摘要 i
英文摘要 ii
圖目錄 iii
表目錄 v
符號說明 vi
章節目次 vii
第一章緒論 1
1.1 前言 1
1.3 研究方法與章節概要 3
第二章盲訊號源分離與稀疏成份分析文獻概要 5
2.1 盲訊號源分離 5
2.2 壓縮感測 8
第三章特徵參數選取 10
3.1 混合係數與矩陣 10
3.2 樣本型態 12
第四章單一頻帶群聚資料與排列問題 14
4.1 K-Means 群聚演算法 14
4.2 排列問題 16
第五章提出方法 18
5.1 第K最近鄰演算法 18
5.3 離群資料重新分配 22
5.4 時頻遮罩分離訊號 24
5.7 壓縮感測插補訊號 25
第六章實驗 31
6.1 實驗環境與設置 31
6.2 分離訊號之效能比較 32
6.3 殘響環境下對分離效果的影響 44
6.4 改變重建字典提升效果 48
第七章結論及未來研究方向 49
參考文獻 50

參考文獻

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指導教授

王家慶(Jia-ching Wang)

審核日期

2012-8-11

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