基於動態時間規整結合線性伸縮之哼唱檢索系統

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姓名

李欣成(Hsin-Cheng Lee) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

基於動態時間規整結合線性伸縮之哼唱檢索系統

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摘要(中)

隨著智慧型手機以及行動網路的普及，使用音樂串流平台或社群網站搜索、下載音樂資料成為日常生活的一部分。對於腦海中有某首歌的旋律卻想不出歌名、歌詞的情況，內涵式音樂檢索系統(Content Based Music Retrieval, CBMR)如哼唱檢索系統就可直接使用歌曲內涵式特徵如旋律、節奏等做為檢索依據，解決上述問題。
對於較大的檢索資料庫，我們必須在辨識準確率與運算時間中做權衡，先前研究以線性伸縮(Linear Scaling, LS)或推土機距離(Earth Mover’s distance, EMD)等準確度較低但運算速度快的方法先過濾掉不相似歌曲，再以高複雜度的動態時間規整(Dynamic Time Warping, DTW)對剩餘歌曲做高精度比對，最後做相似度融合並輸出前十相似的歌曲清單。本研究將LS用在縮短旋律特徵以及在優化模塊中當作微調特徵長度的工具，前者可以減少運算量後者可以使準確度再上升，高精度的DTW則負責計算匹配距離，在歌曲中移動及伸縮匹配窗口找出對應的旋律起始點以及最佳匹配距離，並設計優化模塊及前置過濾器。實驗結果顯示，本系統的MRR值優於先前的研究，線性縮短資料與優化閥值節省了約40%運算時間。

摘要(英)

With the growing popularity of mobile device and internet service, the amount of music data distributed over the internet are increasing everyday. Music information retrieval systems that have capabilities to search for music accurately and quickly are getting more and more attention.
Sometimes users only remember the melody but forget the lyrics, the Content Based Music Retrieval (CBMR) system like QBSH can solve this problem by using features extracted from the music for searching.
To deal with massive retrieval data, we need to balance accuracy and computation time, previous research combines multiple classifiers using score level fusion to reduce computation time, but poor classifier would lead to poor accuracy. Instead of score level fusion method, our proposed system combines DTW and linear scaling(LS), using LS to shorten query and reference songs in different procedure to reduce computation time and using DTW to compute the similarity between query and reference songs. we also design refinement module and pre-filter to enhance the accuracy.
The experiment results show that our method provide higher MRR compared with previous approach, and we reduce about 40% computation time by scaling down the data and finding best threshold setting.

關鍵字(中)

★ 音樂資訊檢索
★ 哼唱檢索
★ 動態時間規整
★ 線性伸縮

關鍵字(英)

★ Music Retrieval
★ Query by singing and humming
★ Dynamic Time Warping
★ Linear Scaling

論文目次

目　錄
摘　要 I
Abstract II
致謝 III
目　錄 Ⅴ
附圖索引 Ⅶ
附表索引 Ⅸ
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
第二章音樂資訊檢索 3
2.1　音樂檢索的種類及特性 3
2.1.1 哼唱檢索現況 4
2.2　哼唱檢索框架 5
2.2.1 哼唱檢索相關研究 8
2.2.2 哼唱檢索特徵 9
2.2.3 MIDI檔案格式介紹 9
2.3　特徵提取方法 10
2.3.1 基頻轉半音 14
2.3.2 空間濾波 15
2.3.3 平滑化處裡(Smoothing) 17
2.3.4 匹配效率 19
2.3.5 向量距離 19
第三章旋律特徵匹配演算法 24
3.1　音調平移(Key transposition) 24
3.2　線性伸縮(Linear Scaling, LS) 27
3.3　推土機移動距離(Earth Mover’s Distance) 29
3.3　動態時間規整(Dynamic Time Warping, DTW) 31
第四章　提出之系統架構 38
4.1　前處理(Preprocessing) 39
4.2　匹配階段 41
4.3　匹配流程 42
第五章　實驗結果分析 48
5.1　實驗環境 48
5.1.1 實驗資料集 49
5.1.2 效能評估方法 50
5.2　實驗設計 50
5.2.1 音高追蹤器參數設定 51
5.2.2 優化模塊(Refinement module)閥值選擇 51
5.2.3 前置過濾器(Pre-filter)閥值選擇 53
5.2.4 最終參數設定(Final setting) 54
5.2.5 線性伸縮對運算時間之影響 54
5.2.6 資料集二之檢索數據 55
5.3　實驗結果比較與分析 56
第六章　結論與未來展望 57
參考文獻 59

參考文獻

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指導教授

張寶基(Pao-Chi Chang)

審核日期

2021-1-29

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