臺灣博碩士論文加值系統

本論文嘗試建立一強健型音樂資訊檢索系統的分析程序。將聽覺生理、聽覺心理與音樂期待三種感知概念融入音樂訊號處理中，以期對於音樂資訊有更強的描述力。此外，使用貝氏統計學的觀點來自動學習得到系統模型中的內涵性參數，可先從音樂樂理設定參數的初始機率分佈，再由觀測到的資料修正至合適的分佈。本論文所要論述的強健性可廣義的設定為：無論在何種系統層級之下，縱使系統輸入存在不可預期之變異性，系統仍可穩健地提供預期中的輸出。和弦進行辨識系統在音樂資訊檢索領域中扮演關鍵的核心角色，因而將以此系統為例進行論述。我們相信本論文的分析程序在音樂資訊檢索領域之應用中將具有一般性。

本論文首先提出一適用於音樂訊號的修正型聽覺感知模型，並依此模型建立新式的音樂特徵。接著，提出一非監督式強健型貝氏和弦進行辨識系統，可辨識單一歌曲的和弦進行且不需任何的訓練資料。兩大部分皆應用於Beatles 13張專輯共180首歌的音樂資料庫，辨識的和弦種類為大小三和弦加無和弦共25種。實驗數據顯示與現存的系統比較皆具有相當優異的表現。

In this thesis, we attempt to propound an analysis procedures of robust music information retrieval (MIR) systems. In order to increase the ability to describe the information of music, we take account of three perceptual phenomenon including auditory physiology, psychoacoustic and music expectation. Furthermore, we use Bayesian statistics to automatic learning the content parameter in the model. In this way, we can begin by setting the initial probability distribution of parameter according to music theory, then fitting to proper distribution in line with observation data. What we wish to demonstrate about robustness can be broadly defined as no matter under what kind of system-level, even if there is unexpected variability in the input, the system can still provide steady expected output. Chord progression recognition system play a critical role of core in the music information retrieval domain. Therefore, we will use this system as an example to be discussed. We believe that the analysis procedures of this thesis will have generality in the field of music information retrieval.

First, we propose a modified auditory perceptual model for music signal processing and use this model to design a novel music feature. Next, we propose an unsupervised robust Bayesian chord progression recognition system that can recognize the chord progression within a single song without requiring any training data. The two parts are used in a total of 180 songs of the Beatles 13 album music corpus containing 25 kinds of chord type in triads major, minor and no chord. The result of the experiment show that our systems have excellent performance compare with the state-of-the-art.

英文摘要 ...........................................................i
中文摘要 ..........................................................ii
致謝 .............................................................iii
1 Introduction ..................................................1
1.1 Motivations ...................................................2
1.2 Chord Progression Recognition .................................2
2 Corpus and Evalution Measures .................................7
2.1 Corpus ........................................................8
2.2 Evaluation Measures ...........................................8
3 Signal Representation for Harmonic Content Analysis ..........10
3.1 Introduction .................................................11
3.2 Chroma Representation ........................................11
3.3 Proposed Chroma Feature Based on Auditory Perceptual Model ...15
3.4 Evaluation and Results .......................................31
4 Chord Progression Recognition Systems ........................33
4.1 Proposed Approaches for Chord Progression Recognition ........34
4.2 Evaluation and Results .......................................58
5 Conclustion ..................................................60
參考文獻 ..........................................................73

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