非對稱摺積神經網路之聲音場景分類

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姓名

伍聿旂(Yu-Chi Wu) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

非對稱摺積神經網路之聲音場景分類
(Asymmetric Kernel Convolutional Neural Network for Acoustic Scenes Classification)

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摘要(中)

隨著人類追求便利性，我們使用電腦使其學習並了解人類所熟知的事物，我們希望通過分析聲音使電腦認識自己的環境，自2013年首次舉辦IEEE Audio and Acoustic Signal Processing (AASP) 聲音場景與事件辨識(Detection and Classification of Acoustic Scenes and Events, DCASE) 競賽，掀起了聲音場景分類 (Acoustic scene classification, ASC)的風波，邁向統一ASC的資料庫與評估方法的第一步，更於2016年舉辦第二屆 DCASE2016競賽。
本論文利用深度學習中的摺積神經網路 (Convolutional Neural Net-work, CNN) 作為ASC的方法。由於CNN之輸入資料為頻譜，而頻譜包含時域資訊與頻域資訊，因此我們假設時域資訊與頻域資訊的資料變化量不一，因此使用長形的摺積核 (kernel) ，也就是本論文提出之非對稱摺積核 (Asymmetric Kernel) (相對於以往的方形的對稱摺積核)，並在訓練期間做資料正規化 (Normalization)加速訓練。我們發現即使現在多以寬又深的網路作為趨勢，發展更佳的資料分類方法，但其實本論文所提出的架構，兩層不用預訓練 (Pre-train)的CNN即可達到相較DCASE2016排名第五名更佳的效果。

摘要(英)

Detection and Classification of Acoustic Scenes and Events (DCASE) Challenge have held in three times. The first DCASE Challenge was held in 2013. Then, DCASE2016 Challenge was the 2nd times of DCASE Challenge. The result why IEEE Audio and Acoustic Signal Processing (AASP) held the 2nd challenge after 3 years is to reset a brand new dataset and united the rule of ASC.
In this work, we use the dataset of ASC from DCASE2016 to propose an Asymmetric Kernel Convolutional Neural Network (AKCNN), whose kernel shape is very different from the traditionally squared kernel. The width and height of the kernel are asymmetric which means that the shape of the kernel is a rectangular kernel. Also, the proposed uses weight normalization (WN) to accelerate the training time because it can early converge the training loss and testing accuracy during training. The best of all, WN can help increase the accuracy of ASC. The result shows that AKCNN achieves accuracy 86.7%. If we rank the score in DCASE2016 ASC Challenge, it would show that we have a better score than the 5th place.

關鍵字(中)

★ 計算聽覺場景分析
★ 聲音場景辨分類
★ 深度學習
★ 摺積神經網路

關鍵字(英)

★ Computational Auditory Scene Analysis
★ Acoustic scenes classification
★ Deep learning
★ Convolutional neural network

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 v
圖目錄 viii
表目錄 xi
第一章緒論 1
1-1 研究動機與背景 1
1-2 論文架構 3
第二章聲音場景分類 4
2-1 聲音場景分類發展史 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與2017聲音場景與事件的分類與偵測競賽 6
2-2 聲音場景分類特徵 7
2-2-1 對數梅爾刻度頻譜 8
2-2-2 梅爾倒頻譜係數 11
第三章神經網路與深度學習 13
3-1 類神經網路 13
3-1-1 類神經網路發展史 14
3-1-2 反向傳播演算法 17
3-2 深度學習 20
3-2-1 深度神經網路 20
3-2-2 摺積神經網路 23
3-3 正規化加速訓練 26
3-3-1 批次資料正規化 26
3-3-2 權重正規化 31
第四章提出之架構 33
4-1 資料前處理 33
4-1-1 特徵提取 34
4-1-2 資料正規化 35
4-1-3 資料切割與堆疊 36
4-2 摺積神經網路架構 37
4-2-1 訓練階段 40
4-2-2 測試階段 41
第五章實驗與分析 43
5-1 實驗環境與資料庫 43
5-2 參數選擇實驗 46
5-3 實驗結果比較與分析 56
第六章結論與未來展望 60
參考文獻 61

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指導教授

張寶基

審核日期

2017-7-26

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