應用深度學習於盲源分離之實現

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姓名

陳又彰(You-Zhang Chen) 查詢紙本館藏

畢業系所

機械工程學系

論文名稱

應用深度學習於盲源分離之實現

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至系統瀏覽論文 (2025-7-1以後開放)

摘要(中)

本論文旨在建構一套盲源分離軟體，並導入深度學習演算法，以此實現音頻擷取時的降噪功能，並於未來應用於智慧製造的領域。
實驗流程方面，本論文先以乾淨的音源訊號進行模型訓練，以此驗證本論文建構之軟體的正確性，再將各式噪音與原始訊號合成，研究應用深
度學習於音頻降噪實現時最佳的模型訓練方式。

摘要(英)

The propose of the thesis is to build a blind source separation program which based on deep learning algorithm. The goal is to achieve noise reduction while capturing sound signals, and to applicate in intelligent manufacturing in the future.
The process of experimentation is training the model with clean audio signal at first. It would verify if the program be written properly. After the verification, the original signal combined with noise would be used to train the model for researching the best training method on noise reduction.

關鍵字(中)

★ 深度學習
★ 盲源分離

關鍵字(英)

★ deep learning
★ blind source separation

論文目次

摘要 i
ABSTRACTii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 viii
符號說明 ix
第 1 章緒論 1
1-1 研究動機與目的 1
1-2 文獻回顧 1
1-3 論文大綱 2
第 2 章研究目標與方法 4
2-1 背景雜音於音頻採集實務之干擾 4
2-2 深度神經網路模型 5
2-2-1 編碼與解碼器架構 5
2-2-2 分離遮罩訓練 6
第 3 章實驗架構 10
3-1 軟體架構 10
3-1-1 檔案路徑擷取 10
3-1-2 模型訓練 11
3-1-3 訓練成果評估 13
3-1-4 訊號分離實現 13
3-2 數據庫選用 13
第 4 章實驗結果及分析 19
4-1 實驗設備 19
4-2 實驗結果 20
4-2-1 無噪音子集模型訓練 21
4-2-2 含噪音成份之音訊分離測試 25
4-2-3 與傳統盲源分離技術成果比較 32
第 5 章結論與未來展望 43
第 6 章參考文獻 45

參考文獻

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〔2〕P. Comon, “Independent component analysis, A new concept?”, Signal Processing, Vol 36, pp. 287-314, April 1994.
〔3〕C. Servière, P. Fabry, “Principal component analysis and blind source separation of modulated sources for electro-mechanical systems diagnostic”, Mechanical Systems and Signal Processing, Vol 19, pp. 1293-1311, 2005.
〔4〕DeLiang. Wang, “Time-frequency masking for speech separation and its potential for hearing aid design”, Trends in amplification, Vol 12, pp. 332-353, 2008
〔5〕C. Jutten, J. H´erault, “Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture”, Signal Processing, Vol 24, pp.1-10, 1991.
〔6〕M.B. Anke, P. Gruber, F. Theis, S. Foo,” Blind source separation based on self-organizing neural network”, Engineering Applications of Artificial Intelligence, Vol 19, pp. 305-311, 2006.
〔7〕I. Valova, N. Gueorguieva, G. Georgiev, “Blind Source Separation with Neural Networks: Demixing Sources From Mixtures with Different Parameters”, 2006 ieee/aiaa 25TH Digital Avionics Systems Conference, pp. 1-11, Portland, USA.
〔8〕F. Acernese, A. Ciaramella, S. De Martino, R. De Rosa, M. Falanga, R. Tagliaferri,” Neural networks for blind-source separation of Stromboli explosion quakes” IEEE Transactions on Neural Networks, Vol 14, pp.167-175, 2003.
〔9〕J. R. Hershey, Z. Chen, J. Le Roux, S. Watanabe, “Deep clustering: Discriminative embeddings for segmentation and separation”, 2016 ICASSP, pp. 31-35, Shanghai, China.
〔10〕V. Nasir, J. Cool, F. Sassani, “Intelligent Machining Monitoring Using Sound Signal Processed With the Wavelet Method and a Self-Organizing Neural Network”, IEEE Robotics and Automation Letters, Vol 4, pp.3449-3456, 2019.
〔11〕Y. Luo, N. Mesgarani, “Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation”, Ieee-Acm Transactions on Audio Speech and Language Processing, Vol 27, pp.1256-1266, 2019.
〔12〕A.G. Howard, Z.Menglong, B. Chen, etc., “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications”, April 2017.
〔13〕V. Panayotov, G. Chen, D. Povey, S. Khudanpur, ” Librispeech: An ASR corpus based on public domain audio books ,2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5206-5210, Brisbane, Australia.
〔14〕W. Gordon, A. Joe, F. Michael, etc., “WHAM!: Extending Speech Separation to Noisy Environments”, 2019.
〔15〕C. Joris, P. Manuel, C, Samuele, etc., “LibriMix: An Open-Source Dataset for Generalizable Speech Separation”, 2020.
〔16〕S.J Choi, A. Cichocki, S.Y Lee, S.Y Lee, “Blind Source Separation and Independent Component Analysis: A Review” Neural Information Processing – Letters and Reviews, Vol 6, November 2004.

指導教授

董必正

審核日期

2020-8-24

推文