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姓名	王書凡(Shu-Fan Wang)  查詢紙本館藏	畢業系所	資訊工程學系
論文名稱	基於複數深層類神經網路之單通道訊號源分離 (Monaural Source Separation Based on Complex-valued Deep Neural Network)
檔案	[Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 ( 永不開放)
摘要(中)	深層類神經網路(Deep neural network, DNN)目前已成為處理訊號源分離問題之熱門方法。其中，幾乎所有以DNN為基礎之分離方法皆只用混合訊號頻譜之能量(Magnitude)做為網路訓練資料，而忽略了相位(Phase)這個隱含在短時傅立葉轉換(STFT)係數中之重要資訊。然而，最近的研究表明，加入相位資訊可以提升分離訊號的聽覺品質。故而在本論文中，我們在進行分離時保留頻譜之相位資訊，從輸入混合訊號中估算目標來源訊號之STFT係數，並視其為一複數域回歸問題。我們發展複數深層類神經網路(Complex-valued Deep neural network)，來學習混合訊號之STFT係數到來源訊號之STFT係數間的非線性映射，做法是利用STFT將混合訊號轉至時頻域後，將其複數STFT係數輸入複數深層類神經網路中，藉此同時考慮能量與相位資訊。此外本論文也提出在成本函數部分加入具有重建性及稀疏性限制式，以提升訊號分離效果。在實驗上，我們將所提出的方法分別應用於語音分離和歌唱分離中。
摘要(英)	Deep neural networks (DNNs) have become a popular means of separating a target source from a mixed signal. Almost all DNN-based methods modify only the magnitude spectrum of the mixture. The phase spectrum is left unchanged, which is inherent in the short-time Fourier transform (STFT) coefficients of the input signal. However, recent studies have revealed that incorporating phase information can improve the perceptual quality of separated sources. Accordingly, in this paper, estimating the STFT coefficients of target sources from an input mixture is regarded a regression problem. A fully complex-valued deep neural network is developed herein to learn the nonlinear mapping from complex-valued STFT coefficients of a mixture to sources. The proposed method is applied to speech separation and singing separation.
關鍵字(中)	★ 深層學習 ★ 盲訊號源分離 ★ 相位	關鍵字(英)	★ Deep Learning ★ Blind Source Separation ★ Phase
論文目次	中文摘要 i Abstract ii 圖目錄 iii 表目錄 v 章節目次 vi 第一章 緒論 1 1.1 背景 1 1.2 研究動機與目的 2 1.3 研究方法與章節概要 2 第二章 基於深層學習之訊號源分離方法及文獻探討 4 2.1 基於感知機之分離方法 5 2.1.1 感知機架構 5 2.2 基於自編碼器之分離方法 8 2.2.1 自編碼器架構 9 2.2.2 時間序列自編碼器 13 2.3 基於遞迴式類神經網路之分離方法 15 2.3.1 遞迴式類神經網路架構 17 2.4 以複數遮罩結合實數深層類神經網路之分離方法 20 2.4.1 複數遮罩推導 20 2.4.2 複數理想比例遮罩在訊號源分離上的應用 21 第三章 基於複數深層類神經網路之訊號源分離 24 3.1 複數深層類神經網路在訊號源分離上的應用 24 3.2 複數深層類神經網路架構 25 3.3 複數深層類神經網路與實數深層類神經網路的比較 26 3.4 複數深層類神經網路之前傳遞推導 27 3.5 複數深層類神經網路之倒傳遞推導 27 3.6 激發函數的討論 36 3.7 成本函數的擴充 39 第四章 複數深層類神經網路應用在音訊分離之實驗 42 4.1 實驗環境與複數深層類神經網路設置 42 4.2 訊號源分離之評估準則 43 4.3 實驗流程 44 4.4 人聲分離實驗結果 45 4.5 歌唱分離實驗結果 47 第五章 結論及未來研究方向 49 第六章 參考文獻 50
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指導教授	王書凡(Jia-Ching Wang)	審核日期	2016-8-25
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