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研究生:黃信傑
研究生(外文):Hsin-Chieh Huang
論文名稱:利用內容可調適之學習機制來完成音源訊號分離
論文名稱(外文):Sound Source Separation Using a Content-Adaptive Learning Mechanism
指導教授:陳自強陳自強引用關係
指導教授(外文):Oscal T.–C. Chen
學位類別:碩士
校院名稱:國立中正大學
系所名稱:電機工程所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2008
畢業學年度:97
語文別:中文
論文頁數:43
中文關鍵詞:獨立成份分析調適性盲蔽訊號分離音源分離
外文關鍵詞:sound sourceblind source separationindependent component analysis
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  • 下載下載:11
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傳統上,獨立成份分析與調適性盲蔽訊號分離已經廣為使用,特別是應用在未知訊號分離(blind source separation)、資料分群(data clustering)、語者辨識、參數擷取等很多地方。本論文主要是先討論空間中2個音源,由2個麥克風中所接收到的混和訊號利用調適性盲蔽訊號分離的原理來分析麥克風訊號中不同的音源成份並將其分離,最後再延伸至4個音源對4個麥克風的情形,而我們在這使用最小互消息演算法(Minimization of the Mutual Information, MMI)來作為分離的演算法,把最小互消息演算法的通式中所使用的機率密度函數用不同的方式做預測,使預測的模型更加接近原訊號的機率分佈,在聲音資料的機率密度函數的預測上對不同的聲音類型(speech、music、noise)採用super-Gaussian function與kernel density function之ㄧ種來做機率密度函數的預測模型,而非原本固定的Gaussian function。從實驗結果中可以發現,對於不同的音源類型以及在音源彼此不同的相關性時,皆使用不同的機率密度函數能使音源訊號分離的效能上有所提升,其中2音源對2麥克風的情形,SIR值平均有2.5dB的提升,而4音源對4麥克風平均也有1.0dB的提升。
Independent component analysis is widely adopted, especially in blind source separation, data clustering, speaker recognition and parameter extraction. This dissertation mainly explores to separate 2 and 4 sound sources from the recorded signals. The mixing signals from 2 and 4 microphones are analyzed by using blind source separation. The Probability Density Function(PDF)associated with the Minimization of the Mutual Information (MMI) is adapted according to the correlation and characteristics of the mixing signals. The correlation of the separated sources is investigated to determine the adequate PDF from the super-Gaussian function and kernel density function. Additionally, separated sound signals are classified into speech, audio, noise and so on in which one of the super-Gaussian function and kernel density function is adequately selected to conduct MMI rather than the Gaussian function. Such an approach allows our prediction model closer to the PDF of the original signals. In our experiments, different kinds of sound signals with different correlations are employed to verify our adaptive model. The proposed adaptive model selecting the adequate PDF can effectively improve the correctness of sound source separation. The SIR values are improved around 2.5 and 1.0dB in average for the situations of 2 sources to 2 microphones and 4 sources to 4 microphones, respectively. Therefore, the proposed adaptive model used in MMI of blind source separation can be widely applied to various independent component analyses.
第一章 序論 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 獨立成份分析與調適性盲蔽訊號分離相關演算法 3
2.1 前言 3
2.2 獨立成份分析 3
2.2.1 獨立成份分析基礎理論 3
2.2.2 獨立成份分析的主要架構 4
2.2.2.1 資料前處理 5
2.2.2.2 獨立性 6
2.2.2.3 量測方式 7
2.3 調適性盲蔽訊號分離 9
2.3.1 在時域上一般式的區段盲蔽訊號分離演算法 11
2.3.2 價值函數與演算法推導 14
第三章 新穎之判斷機制用於音源訊號分離演算法 16
3.1 前言 16
3.2 預測模型 17
3.2.1 最小互消息 17
3.2.2 廣義高斯機率密度函數 17
3.2.3 核心機率密度函數 18
3.2.4 以不同預測模型測試 19
3.3 音源分類 24
3.3.1 前言 24
3.3.2 特徵值 24
3.3.2.1 短時距能量函數 24
3.3.2.2 短時距越零率函數 25
3.3.2.3 短時距基頻 25
3.3.3 分類順序 26
3.3.3.1 偵測靜音 26
3.3.3.2 區分合諧與非合諧 27
3.3.3.3 偵測音樂部份 27
3.3.3.4 偵測語音部份 27
3.4 推廣至4音源對4麥克風 29
第四章 實驗結果與討論 30
4.1 實驗環境架設與說明 30
4.2 二音源對應二麥克風 31
4.3 四音源對應四麥克風 38
第五章 結論與未來工作 41
參考文獻 42
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