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研究生:曾郁豪
研究生(外文):Yu-Hao Tseng
論文名稱:基於平行膠囊神經網路之聲音事件偵測
論文名稱(外文):Parallel Capsule Neural Networks for Sound Event Detection
指導教授:張寶基
指導教授(外文):Pao-Chi Chang
學位類別:碩士
校院名稱:國立中央大學
系所名稱:通訊工程學系
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:2019
畢業學年度:107
語文別:中文
論文頁數:71
中文關鍵詞:計算聽覺場景分析聲音事件偵測深度學習膠囊神經網路
外文關鍵詞:Computational Auditory Scene AnalysisSound Event DetectionDeep learningCapsule neural network
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人工智慧的研究過去60多年來從未停歇,隨著科技的日新月異,我們希望電腦可以像人類一樣具備學習能力,近年來因電腦圍棋alpha go一戰成名,讓更多人投入機器學習 (Machine Learning) 以及深度學習 (Deep Learning) 之領域,因此也發展出許多不同的網路架構,透過這些網路架構來讓電腦輔助人類對資料進行判斷與分類偵測。
本論文利用深度學習中的膠囊神經網路 (Capsule Neural Network, CapsNets) 作為方法,提出應用於聲音事件偵測的系統。將所提取的特徵,透過向量的方式丟入神經網路進行訓練,除了膠囊網路本身可以有效的辨別重疊事件,我們再將膠囊網路拓展為平行的膠囊網路,使每單個膠囊可以學習到更多的特徵,透過以上方法相比於DCASE 2017的Baseline錯誤率下降約41%,而與DCASE 2017 競賽第一名之架構相比,錯誤率也下降26%左右。
The research of artificial intelligence has never stopped for more than 60 years. With the rapid development of technology, we hope that computers can have the same learning ability as human beings. In recent years, more and more people invest in the field of machine learning and deep learning, because of the success of the alpha go. Many different network architectures have been developed to allow computers to assist humans in detecting and classifying data.
We used the Capsule Neural Network (CapsNets) in deep learning as a method. Propose a system for sound event detection. The extracted features are sent to the neural network for training through the vector. In addition to capsule network can effectively identify overlapping events, we expand the capsule network into a parallel capsule network, let per capsule can learn more features. Compared with DCASE 2017 Baseline, our proposed method error rate is reduced by about 41%. Compared with the architecture of the first place in DCASE 2017 challenge, the error rate also dropped by about 26%.
摘要 i
Parallel Capsule Networks for Sound Event Detection Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 viii
第一章 緒論 1
1-1 研究動機與背景 1
1-2 論文架構 3
第二章 聲音事件偵測 4
2-1 聲音場景分類及事件偵測發展史 4
2-1-1 2013聲音場景與事件的分類與偵測競賽 5
2-1-2 2016至2019聲音場景與事件的分類與偵測競賽 6
2-2 聲音事件偵測特徵 7
2-2-1 對數梅爾刻度頻譜 8
2-2-2 梅爾倒頻譜係數 9
2-3 聲音事件偵測困難 11
第三章 神經網路與深度學習 12
3-1 類神經網路 12
3-1-1 類神經網路發展史 13
3-1-2 多層感知機 16
3-2 深度學習 21
3-2-1 深度神經網路 21
3-2-2 摺積神經網路 22
3-3 膠囊神經網路 25
3-3-1 膠囊神經網路vs摺積神經網路 25
3-3-2 Vector in Vector out 27
3-3-3 動態路由 29
3-3-4 膠囊網路架構 31
3-3-5 膠囊神經網路之聲音事件偵測 34
第四章 提出之架構 36
4-1 特徵提取 36
4-2 基於聲音事件偵測之膠囊神經網路架構 37
4-3 摺積和池化涵蓋範圍 39
4-4 平行膠囊神經網路架構 41
第五章 實驗與分析 46
5-1 實驗環境與資料庫 46
5-2 評分機制 49
5-3 實驗結果比較與分析 51
第六章 結論與未來展望 55
參考文獻 56
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