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研究生:陳世豪
研究生(外文):Shih-Hao Chen
論文名稱:利用音色特徵及支持向量機於內涵式音樂分類系統之設計與實現
論文名稱(外文):Content-based Music Genre Classification Using Timbral Feature Vectors and Support Vector Machine
指導教授:柯松源
指導教授(外文):Sung-Yuan Ko
學位類別:博士
校院名稱:義守大學
系所名稱:資訊工程學系
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:2016
畢業學年度:104
語文別:英文
論文頁數:41
中文關鍵詞:最佳基演算法小波包轉換音樂曲風分類支持向量機稀疏表示分類器
外文關鍵詞:best basis algorithmwavelet packet transformmusic genre classificationsparse representation based classification
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本論文主要研究一種具自動分析之新型音樂曲風類型分類技術,該技術結合小波包轉換(wavelet packet Transform,WPT)、最佳小波包基的選取(best wavelet packet basis via best basis algorithm,BBA)、改良式梅爾倒頻譜係數(modified-MFCC)及分類器(Classifier)運用來有效地改善音樂曲風分類問題。在音樂曲風分類技術上,本文先使用兩種常見的濾波器分析技術如移動平均濾波器(moving average filter)及巴特渥斯低通濾波器(Butterworth low-pass filte)來部份地刪除短時距訊號分割之波動現象,接著使用小波包轉換(WPT)及改良式梅爾倒頻譜係數將音樂資料中的音色特徵如低階特徵及高階特徵擷取出來,然後根據被擷取出的音色特徵將音樂資料分類到各個音樂的類別。ISMIR2004 GENRE database是本實驗中所使用的音樂資料庫,該資料庫為2004 年音樂曲風分類競賽(ISMIR2004 Music Genre Classification Contest)所使用,此資料庫共1458首計六類曲風的音樂檔案可以被用來評估比較各種音樂資料分類演算法的良寙。根據實驗結果顯示,本論文所提之技術對於音樂分類辨識率可達89.7%。

This dissertation studies a new music genre classification algorithm based on wavelet packet Transform (WPT), best wavelet packet basis via best basis algorithm (BBA), modified Mel-frequency cepstral coefficient (modified-MFCC), and Classifier to accurately classify and increase classification performance. In the proposed music genre classification technique, moving average filter and Butterworth low-pass filter are first applied to partly eliminate the effect of fluctuation in short-term signal. Then WPT and modified-MFCC are used to extract timbre features, such as the low-level feature and high-level feature, to accomplish music genre classification. In our experiments we used a dataset of the ISMIR2004 GENRE classification contest. The dataset consist of 1458 songs in 6 classes, is used to evaluate the performances of music genre classification method against other similar schemes. Experimental results show that the proposed new music genre classification algorithm could achieve the average classification accuracy rate of 89.7%.

ABSTRACT I
摘要 II
List of Figures IV
I. Introduction 1
II. TIMBRAL FEATURES 4
III. PROPOSED MUSIC GENRE CLASSIFICATION SYSTEM 5
A. Moving average filter and Butterworth low-pass filter 6
B. Introduction to the Wavelet Package Transform 8
C. Introduction to the Best Basis Algorithm 9
D. Feature Extraction 13
E. Discrete Trigonometric Transforms 17
F. Introduction to Support Vector Machine 19
G. Introduction to the Sparse Representation based Classification 21
IV. Experimental Results 23
A. Datasets 23
B. Classification Results 23
V. CONCLUSIONS 29
VI. References 30

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