基於時頻感知域經由深度信念網路之吉他彈奏技巧辨識

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姓名

劉郁廷(Yu-ting Liu) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

基於時頻感知域經由深度信念網路之吉他彈奏技巧辨識
(Recognition of Guitar Playing Techniques with Deep Belief Networks based on Spectral-Temporal Receptive Fields)

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摘要(中)

吉他是非常常見的樂器，被廣泛運用於流行音樂、搖滾樂、民謠…等，學習吉他成為許多人的興趣。而不同吉他彈奏技巧能夠表現不同聲音、展示不同情緒，進而拼湊成一幅樂章。
吉他彈奏技巧的變化相當細微，欲將其分類、辨識是具有挑戰性的工作。對於不熟悉吉他的人而言，技巧聽起來十分相像；而會彈吉他的人，便能單憑聆聽就區分出不同技巧。
面對彈奏技巧些微的變化，本研究提出以深度學習網路(Deep Belief Networks, DBN)學習音訊特徵，包含梅爾倒頻譜系數(MFCCs)及大腦皮質組織(spectro-temporal receptive field)，藉由不同初始化方法與新提出的深度學習網路架構，學習找出相對關鍵的特徵增加辨識效果，並使用完整音檔和Onset部分進行比較。實驗結果顯示，本研究提出之方法於Onset部分最高提升11.74%之辨識率，而完整音檔的部分，辨識率更為精準，到達0.9819。說明有效運用特徵參數及辨認器，相較於大量參數，更能準確分類資訊。

摘要(英)

Guitar is a very common instrument which has been widely used in popular music, rock, ballad, etc. Different guitar playing technique can perform various vocal, express different emotion, then play the wonderful music. Some of guitar playing techniques has only tiny difference. To recognize it is a big challenge. This paper proposed a guitar playing technique recognition system including a novel STRF based feature extraction algorithm and a novel deep learning model called HCDBN. In experiments, the proposed system improves 11.74% recognition rate than baseline system on onset version dataset and achieves 98.19% recognition rate on whole version dataset. This paper also make an onset detection based guitar technique recognition system which can applied in real world guitar solo music.

關鍵字(中)

★ 聽覺模型
★ 吉他彈奏技巧
★ 分類
★ 辨識
★ 類神經網路
★ 深度學習

關鍵字(英)

★ STRF
★ Guitar Playing Technique
★ Classification
★ Recognition
★ Neural Network
★ Deep Belief Network

論文目次

摘　要 I
Abstract II
致謝 III
目　錄 IV
附圖索引 VI
附表索引 VIII
第一章緒論 1
1.1　研究背景 1
1.2　研究動機與目的 2
1.3　論文架構 2
第二章聽覺感知模型 3
2.1　聽覺感知模型 3
2.2　初期耳蝸模型 4
2.3 大腦皮質模型 6
2.4 STRF參數擷取 8
2.4.1 Scale參數擷取 8
2.4.2 Rate參數擷取 8
第三章深度信念網路 11
3.1　深度信念網路 11
3.2 Generative Restricted Boltzmann Machines 15
3.3 Discriminative Restricted Boltzmann Machines 19
3.4 Initialization 21
3.5 Softmax 22
第四章　DBN架構與實驗結果 24
4.1　系統架構 24
4.1.1 Scheme1：Original DBN 24
4.1.2 Scheme2：LDDBN 26
4.1.3 Scheme3：LFDBN 27
4.1.4 Scheme4：HDDBN 28
4.1.5 Scheme5：HCDBN 29
4.1.6 架構比較 31
4.2　實驗數據 32
4.2.1 五種架構在Split資料庫的結果 35
4.2.2 Split資料庫以STRF參數擷取 36
4.2.3 觀察一：Onset部份的時頻圖變異數 37
4.2.4 五種架構在Whole資料庫的結果 39
4.2.5 Whole資料庫以STRF參數擷取 40
4.2.6 觀察二：完整音檔的時頻圖變異數 40
4.2.7 分為19子類別的結果 43
4.2.8 Split資料庫所有架構、參數、分類之組合比較總表 45
4.2.9 Whole資料庫所有架構、參數、分類之組合比較總表 48
4.2.10 真實音檔 52
第五章　結論及未來展望 55
參考文獻 56

參考文獻

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指導教授

張寶基、王家慶(Pao-chi Chang Jia-Ching Wang)

審核日期

2015-7-28

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