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研究生:易洋
研究生(外文):Yang Yi
論文名稱:使用CNN為基礎之特徵擷取器及複合分類器結構之音樂曲風分類
論文名稱(外文):Music Genre Classification Using CNN-based Feature Extractor and Compound Classifier Structures
指導教授:古鴻炎古鴻炎引用關係陳冠宇陳冠宇引用關係
指導教授(外文):Hung-Yan GuKuan-Yu Chen
口試委員:王新民林伯慎
口試委員(外文):Hsin-Min WangBor-Shen Lin
口試日期:2019-01-30
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:資訊工程系
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
中文關鍵詞:音樂曲風分類多層分類器結構分類器混合結構CNN特徵提取梅爾刻度頻譜梅爾倒頻譜係數
外文關鍵詞:music genre classificationhierarchical classifier structureclassifier-mixing structureCNN feature extractormel-frequency spectrumMFCC
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本論文研究了階層式及分類器混合這兩種複合式的分類器結構,用於提升音樂曲風分類之正確率。首先我們對輸入的樂曲分析出四種頻域特徵,分別是梅爾頻譜,梅爾倒頻譜,調變頻譜及打擊音效頻譜,作為基礎聲學特徵;然後使用平均值與標準差、主成份分析以及卷積神經網路來對基礎聲學特徵作降維處理,並提取出進階之聲學特徵向量。接下來,我們會拿數種方式求得的進階聲學特徵向量,去訓練四種基本的分類器,就是支援向量機、K-近鄰算法、高斯混合模型與多層感知器。然後,我們經實驗選用四種基礎聲學特徵所對應的最佳分類效能之降維方法與基本分類器的組合,去構建階層式分類器結構;另外,我們使用四種基礎聲學特徵,分別訓練出一個對應的基於卷積神經網路的專家網路,用來構建分類器混合之結構。曲風分類之實驗結果顯示,兩種複合的分類器結構在不同的資料集上,皆可改進曲風分類的正確率,階層式分類器結構最高可獲得87.1%之正確率,而分類器混合結構則可獲得最高88.0%之正確率。
In this thesis, we have studied two types of compound classifier structures, hierarchical structure and classifier-mixing structure, in order to improve the accuracy of music genre classification. First, four kinds of spectral features are analyzed from an inputted music signal, including mel-frequency spectrum, mel-frequency cepstrum, modulation spectrum and percussive spectrum. These features are considered as basic acoustic features (BAF). Then, three dimension-reduction methods, mean & standard deviation, principal component analysis (PCA), and convolutional neural network (CNN), are used to extract advanced acoustic features (AAF) from BAF. Next, we use AAF to train four types of basic classifiers, i.e. support vector machine, k-nearest neighbor, Gaussian mixture model and multiple layer perceptron. By combing different AAF and basic classifiers, we pick one best-performance combination of AAF and basic classifier for each kind of BAF. Then, the four best-performance combinations are used to construct a hierarchical classifier structure. On the other hand, each kind of BAF is used to train a corresponding expert network based on CNN. Then, the four expert networks are used to construct a classifier-mixing structure. According to the results of music genre classification experiments with different datasets, both compound classifier structures studied here can obtain considerable improvement in classification rate. The hierarchical classifier structure achieves the classification accuracy, 87.1% whereas the classifier-mixing structure achieves the higher classification accuracy, 88.0%.
目錄
ABSTRACT IV
摘要 1
第1章 緒論 2
1.1 研究動機 2
1.2 研究方法 3
第2章 文獻回顧 9
2.1 音訊資料之表示 9
2.1.1 樣本點單位之特徵 9
2.1.2 音框單位之特徵 10
2.2 基礎分類方法 13
2.3 分類效能提升之方法 15
2.4 與本論文直接相關之文獻 17
2.4.1 CNN提取進階聲學特徵 17
2.4.2 調變頻譜在音樂曲風分類的應用 21
第3章 基礎聲學特徵與降維方法 24
3.1 資料集準備 24
3.1.1 GTZAN資料集 24
3.1.2 FMA資料集 25
3.2 音訊預處理 27
3.3 基礎聲學特徵 29
3.3.1 梅爾頻譜 29
3.3.2 梅爾倒頻譜 31
3.3.3 調變頻譜 32
3.3.4 打擊音效頻譜 33
3.3.5 程式實作 35
3.4 特徵降維方法 37
3.4.1 平均值與標準差 37
3.4.2 主成分分析 38
3.4.3 卷積神經網路 41
第4章 基本分類器 43
4.1 高斯混合模型 43
4.2 支援向量機 46
4.3 K-近鄰算法 48
4.4 多層感知器 49
第5章 分類器複合之結構 50
5.1 階層式分類器結構 50
5.2 分類器混合結構 53
第6章 實驗及討論 57
6.1 基本分類器之實驗 57
6.1.1 正規化 57
6.1.2 特徵降維 57
6.1.3 曲風分類正確率 58
6.2 階層式結構之實驗 61
6.3 混合結構之實驗 63
6.3.1 專家分類器之實驗 63
6.3.2 專家網路混合之實驗 64
第7章 結論 68
參考文獻 71
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