請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/7570
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 王昭男 | |
dc.contributor.author | Yu-Wei Lin | en |
dc.contributor.author | 林昱偉 | zh_TW |
dc.date.accessioned | 2021-05-19T17:46:43Z | - |
dc.date.available | 2021-07-17 | |
dc.date.available | 2021-05-19T17:46:43Z | - |
dc.date.copyright | 2018-07-17 | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018-07-06 | |
dc.identifier.citation | [1] T. W. Anderson, “An introduction to multivariate statistical analysis”, WILEY, Columbia University, 1958
[2] J. K. Nicholson, J. C. Lindon and E. Holmes, “ Metabonomics : understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data”, xenobiotica , Vol.29, no.11, 1181-1189, 1999 [3] 孟哲賢, “以多變量分析法探討現行發布之生物毒性試驗”, 國立交通大學環境工程研究所碩士論文, 2012 [4] 李建生, “應用多變量分析法於台灣主要河川流域特性之研究”, 中國文化大學地學研究所碩士論文, 2006 [5] 黃俊傑, “運用多變量分析探討金融市場之消費者決策行為”, 朝陽科技大學財務金融系碩士論文, 2007 [6] A. J. Izenman, “Linear Discriminant Analysis”, Modern Multivariate Statistical Techniques, Springer, 237-280, 2013 [7] P. N. Tan, “Cluster Analysis Basic Concepts and Algorithms”, Data Mining, Pearson, 2004 [8] B. Thompson, “Canonical correlation analysis”, Reading and understanding MORE multivariate statistics, 285-316, 2000 [9] K. Pearson, “On Lines and Planes of Closest Fit to Systems of Points in Space”, Philosophical Magazine, Vol.2, no.6, 559–572, 1901 [10] H. Abdi, Williams, L.J., “Principal component analysis”, Wiley Interdisciplinary Reviews: Computational Statistics, Vol.2, 433–459, 2010. [11] 黃金鷗, “即時人臉偵測及辨識系統的開發”, 國立台北科技大學自動化科技研究所碩士論文, 2006 [12] P. Comon., “Independent component analysis, A new concept?”, Signal Processing, Vol.36, 287-314, 1994 [13] A.J. Bell and T.J. Sejnowski, “An information-maximisation approach to blind separation and blind deconvolution.”, in Advances in Neural Information Processing System 7, 467-474.The MIT Press, Cambridge, MA, 1995 [14] A.J. Bell and T.J. Sejnowski, “A Non-linear Information Maximisation Algorithm that Performs Blind Separation” Neural Computation, Vol.7, 1129-1159, 1995 [15] A. Hyvärinen, “A Fast Fixed-Point Algorithm for Independent Component Analysis,” Neural Computation ,Vol.9, no.7, 1489-1492, 1997 [16] Ella Bingham and Aapo Hyvärinen, “A fasted-point algorithm for independent component analysis of complex valued signals, ” Neural Networks Research Centre, 19th, January, 2000 [17] A. Hyvärinen and E. Oja, “Independent Component Analysis:Algorithms and Applications, “Neural Networks ,Vol.3, no.4-5, 411-430, 2000 [18] 馬超,呂志強,章林柯, “基於BSS的含噪聲機械振動信號分離研究”, 噪聲與振動控制, Vol.30, no.6, 161-164, 2010 [19] 陳建州, “利用獨立成份分析法在區域特徵上的人臉辨識”, 國立成功大學資訊工程學系碩士論文, 2004 [20] S. Makeig, A. J. Bell, T. P.Jung, T. J. Sejnowski, “Independent component analysis of electroencephalographic data”, Advances in Neural Information Processing Systems 8, 145-151, 1995 [21] T.W.Lin, M. Girolami and T. J.Sejnowski, “Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Sub Gaussian and Super Gaussian Sources”, Neural Computation, Vol.11, no.2, 409-433, 1999 [22] 連憶如, “頻域獨立成分分析法於語音訊號分離之研究”, 國立交通大學電機與控制工程所碩士論文, 2004 [23] S. Kurita, H. Saruwatari, S. Kajita, K. Takeda and F. Itakura, “Evaluation of blind signal seperation method using directivity pattern under reverberant conditions,” in Proc. ICASSP2000, 3140-3143, 2000 [24] N.Murata, S.Ikeda and A.Ziehe, “An approach to blind source separation based on temporal structure of speech signals ”, Neurocomput ,Vol.41 ,1-24 ,2001 [25] 張嘉芳, “以FastICA為基礎之時域聲音分離演算法, ”國立交通大學電機與控制工程研究所碩士論文,2003 [26] Y. Ephraim and H. L.Van Trees “A signal subspace approach for speech enhancement” , IEEE Transactions on Speech and Audio Processing, Vol.3, no.4, 1995 [27] 陳淼海, “基於盲訊號分離語音增強技術之遠距離雜訊語音辨識”, 國立成功大學電機工程學系碩士論文, 2009 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/7570 | - |
dc.description.abstract | 音訊分離一直是訊號處理上想要達成的目標,若能從眾多音訊中擷取出自己需要的訊號,在後續也有相當廣泛的應用,本文利用獨立成分分析法,對未知聲源的訊號進行訊號資料分析,以解決盲訊號分離問題。
獨立成分分析法係假設多支麥克風的量測訊號是由多個不同聲源訊號受空間因素影響後混合而成。故本文利用快速獨立成分分析法,針對以兩支麥克接收兩個聲源訊號的情況進行訊號解混合之演算,輸出接近原本各聲源訊號的結果。 在現實情況中,時域訊號會因聲源到麥克風的距離差產生時間延遲的情形,導致分離結果不佳。為了避免此問題,以往的研究大多利用傅立葉轉換將訊號轉至頻率域後再對各頻帶訊號進行分離,但是獨立成分分析法存在不確定性,會導致在將各頻帶分離後訊號加總還原時產生混淆,故需搭配其他理論進行更大量的計算使分離結果更準確。 本文的重點為增加演算法之前處理和後處理步驟,配合獨立成分分析法的特性對輸入訊號先進行時間位移處理,即可改善分離效果,並利用子空間語音增強法對分離後的訊號進行優化。除此之外,本文也對不同聲源組合進行實驗,並對分離結果作分析與比較,最後將整個演算流程以程式實現。 | zh_TW |
dc.description.abstract | Audio separation has always been the goal of signal processing. If we can extract the signals we need from many audio sources, and have a wide range of applications in the future, this paper uses independent component analysis to signal the signals of unknown sound sources. Analysis to solve the problem of blind signal separation.
Independent Component Analysis(ICA) is the common algorithm to solve Blind Source Separation(BSS) problem. By using iteration algorithm, ICA can estimate the most optical demixing matrix for mixed signal. Theoretically, ICA can separate each voice which is made by different source from measured signal which are mixed. However, using time domain ICA algorithm will cause time delay difference problem because the distance between the signal source and each sensor is different. Even though we can transform measured signals into frequency-domain by Fourier Transform and avoid the problem, the ambiguities of ICA will cause dilation problem and permutation problem. The topic of paper is adding pre-processing step for solving and time delay difference problem. In addition, we use subspace speech enhance as post-processing to optimize ICA result. | en |
dc.description.provenance | Made available in DSpace on 2021-05-19T17:46:43Z (GMT). No. of bitstreams: 1 ntu-107-R05525023-1.pdf: 3896564 bytes, checksum: 264202de6237f786ee10ade2cfa4680e (MD5) Previous issue date: 2018 | en |
dc.description.tableofcontents | 誌謝 i
摘要 ii ABSTRACT iii 目錄 iv 圖目錄 vi 表目錄 ix 第一章 緒論 1 1.1 研究動機 1 1.2 文獻回顧 3 1.3 論文架構 5 第二章 獨立成分分析法基礎理論 6 2.1 BSS問題描述 6 2.2 ICA假設限制 8 2.3 ICA不確定性 10 2.3.1 大小不確定性 10 2.3.2 排序不確定性 11 2.4 ICA演算法 13 2.4.1 ICA前處理 14 2.4.2 中央極限定理(CLT) 18 2.4.3 非高斯分佈量測 19 2.4.4 實數FastICA演算法 25 2.5 FastICA模擬 29 第三章 時域音訊分離架構 31 3.1 時間延遲差(Time-delay difference)問題 31 3.2 頻率域ICA 35 3.3.1 頻率域ICA流程 35 3.3.2 ICA不確定性於頻率域造成之問題 36 3.3 Signal shifting FastICA 38 3.3.1 建立正常模式 38 3.3.2 Signal shifting FastICA演算法 42 3.3.3 Signal shifting FastICA測試 45 3.4 子空間增強法 48 第四章 時域音訊分離實驗 53 4.1 實驗儀器架設 53 4.1.1 實驗設備 53 4.1.2 實驗架設 54 4.1.3 音源挑選 55 4.2 時域音訊分離演算法流程 58 4.3 實驗結果與討論 59 4.3.1 兩非高斯分佈性強之聲源組合 59 4.3.2 非高斯分佈性一強一弱聲源組合 64 4.3.3 相關性不同之聲源組合比較 66 4.3.4 移動聲源實驗 70 第五章 結論與未來展望 73 5.1 結論 73 5.2 未來展望 74 參考文獻 75 | |
dc.language.iso | zh-TW | |
dc.title | 獨立成分分析結合子空間增強於時域音訊分離之分析探討 | zh_TW |
dc.title | A Processing of Time Domain Audio Signal Seperation Based on FastICA and Subspace Signal Enhancement | en |
dc.type | Thesis | |
dc.date.schoolyear | 106-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 謝傳璋,宋家驥,余仁方 | |
dc.subject.keyword | 盲訊號分離,獨立成分分析法,中央極限定理,子空間增強法, | zh_TW |
dc.subject.keyword | Blind Source Separation,Independent Component Analysis,Central Limit Theorem,subspace speech enhancement, | en |
dc.relation.page | 77 | |
dc.identifier.doi | 10.6342/NTU201801352 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2018-07-06 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
顯示於系所單位: | 工程科學及海洋工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-107-1.pdf | 3.81 MB | Adobe PDF | 檢視/開啟 |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。