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| Title: | 小波理論於曲風辨識上之應用
The Application of Wavelet Transform on Automatically Musical Genre Classification |
| Authors: | 陳彥名 |
| Contributors: | 曾正男
陳彥名 |
| Keywords: | 小波轉換
線性判別分析
離散餘弦轉換
決策樹
曲風辨識 |
| Date: | 2015 |
| Issue Date: | 2015-10-01 14:17:32 (UTC+8) |
| Abstract: | 隨著科技的進步，網際網路已充斥在我們的生活之中。音樂也不再以硬體儲存的方式流傳（例如ＣＤ、黑膠唱片），而是轉變為數位音樂的方式，透由網路平台散播。許多數位音樂串流服務平台網站也如雨後春筍般誕生，例如iTunes、Spotify、Musicovery。加上文化水平的提升，音樂已是現代人生活之中，不可或缺的一部分。世界上的音樂難以計數，如何將音樂分門別類做好管理乃為現代商業應用的一個重要課題。因此，音樂曲風自動化辨識的技術確實為一個實用且難以迴避的課題。
過去在曲風自動化辨識已有許多研究，但內容不外乎音訊處理、頻譜轉換、特徵擷取、特徵降維、監督式學習機。在相同的模式下提出各種改良，或是全新的特徵擷取…諸如此類，而辨識率也達到了七成以上。本篇論文採用不同於以往的做法，將訊號進行頻譜轉換後層層降維，所得之訊號搭配LDA與決策樹進行辨識，最後去比較與分析離散餘弦轉換與小波轉換在辨識率上的優劣。我們發現搭配小波轉換與混合LDA及決策樹的方法，可以將音樂曲風之分辨率達到八成五以上。
目錄
口試委員會審定書.................................................................................................................. i
致謝.......................................................................................................................................... ii
中文摘要.................................................................................................................................. iii
Abstract .................................................................................................................................... iv
目錄.......................................................................................................................................... vi
表目錄...................................................................................................................................... viii
圖目錄...................................................................................................................................... ix
第一章緒論....................................................................................................................... 1
第一節研究背景與動機..................................................................................... 1
第二節研究目的................................................................................................. 2
第三節研究架構................................................................................................. 3
第二章文獻探討.............................................................................................................. 4
第一節前言......................................................................................................... 4
第二節預處理..................................................................................................... 5
第三節音樂特徵擷取......................................................................................... 7
一、梅爾倒頻譜係數（Mel Frequency Cepstral Coefficients, MFCC）
................................................................................................................. 8
二、雷尼熵值（Renyi Entropy, RE） .................................................. 9
三、頻譜質心（Spectral Centroid, SC）.............................................. 9
四、強度與音色（Intensity and Timbre） ........................................... 9
第四節建置分類器............................................................................................. 11
一、支持向量機（Support Vector Machines, SVM） ......................... 11
二、最近鄰居法（k-Nearest Neighbors algorithm, k-NN）................ 12
三、高斯混合模型（Gaussian Mixture Models, GMM）................... 13
第三章降維方法.............................................................................................................. 14
第一節小樣本分析............................................................................................. 14
第二節音訊分析與k-means 演算法................................................................. 16
第三節頻譜與降維............................................................................................. 17
第四節線性判別分析......................................................................................... 21
一、監督式維度縮減（Supervised Dimension Reduction）............... 21
二、LDA 公式推導............................................................................... 22
三、LDA 實驗結果............................................................................... 28
第四章實驗方法.............................................................................................................. 30
第一節挑選實驗音樂樣本................................................................................. 31
第二節音訊處理................................................................................................. 33
第三節維度縮減................................................................................................. 34
第四節隨機七三分配......................................................................................... 34
第五節線性判別分析之降維與預測................................................................. 35
第六節離散小波轉換......................................................................................... 36
第七節系統決策樹............................................................................................. 42
第八節混合系統................................................................................................. 44
一、Classical - Classical ........................................................................ 45
二、Classical - Electron ......................................................................... 45
三、Classical - Rock .............................................................................. 45
四、Classical - Pop................................................................................. 46
五、Classical - Vocal Pop ...................................................................... 46
六、其餘情境......................................................................................... 48
第九節混合系統的最終決策............................................................................. 50
第五章結論與未來展望............................................................................................... 54
參考文獻.................................................................................................................................. 55 |
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| Description: | 碩士
國立政治大學
應用數學研究所
101751014 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0101751014 |
| Data Type: | thesis |
| Appears in Collections: | [應用數學系] 學位論文
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