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Gerhard Widmer

Using AI and Machine Learning to study Expressive Music Performance: Project Survey and First Report. Gerhard Widmer. Keywords:. Machine Learning Data Mining Expressive Music Performance. Outline. 1. Introduction 2. Expressive Music Performance 3. The Base Research Framework

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Gerhard Widmer

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  1. Using AI and Machine Learning to study Expressive Music Performance:Project Survey and First Report Gerhard Widmer

  2. Keywords: • Machine Learning • Data Mining • Expressive Music Performance

  3. Outline 1. Introduction 2. Expressive Music Performance 3. The Base Research Framework 4. A Brief Activity and Status Report 5. A New Data Mining Approach 6. Conclusion

  4. 1. Introduction

  5. Apply AI methods • Developing computational methods • Use machine learning and data mining • Build formal models

  6. 2. Expressive Music Performance

  7. Make music moving • Detectpatter and regularities • Performing artist • Large Collections of performances

  8. 3. Inducting Performance Models from Real performances : The Base Research Framework

  9. 1.由音樂家的表演獲得高品質的演出，並轉成機器可讀的1.由音樂家的表演獲得高品質的演出，並轉成機器可讀的 形式 • 2.將音符編碼成機器可讀的形式並對應到樂譜中 • 3.取出拍子，響度分別比較從樂譜和演奏者實際表現，並 把這些數據轉換成電腦可以分析 • 4.分析音樂結構(韻律,音調) • 5.開發機器學習的演算法，然後有系統的連接音樂結構方 面典型表達的特徵，還有制訂他們的符號規則 • 6.從不同的音樂風格中執行有系統的實驗 • 7.從它們的性質去分析學習結果

  10. 4. A Brief Activity and Status Report

  11. 4.1 Real-world Performance Data • It is impossible to extract precise performance information. • The main source of performance data are special pianos. ( Bosendorfer SE290)

  12. 4.2 Score and Expression Extraction • Beat induction • Quantization • Inferring the correct or intended enharmonic spelling of notes.(eg : G# VS. Ab)

  13. 4.3 Musical Structure Analysis • Segmentation • Categorization and motiuic analysis • Implication Realization Model

  14. 4.4 Mode Building via Inductive Learning : Initial Investigations • Settings of the rule parameters better then baseline.

  15. 5. Learning Partial Characterizing Models : A New Data Mining Approach

  16. 5.1 The Goal : Learning Partial Models • Partial Characterizing Models.

  17. 5.2 Data and Target Concepts • In the timing dimension • In dynamic • In articulation

  18. 5.3 The PLCG Rule Discovery Alogrithm

  19. 5.4 Some Simple Principles Discovered

  20. 5.5 Quantitative Evaluation

  21. Conclusion • 精確的測量出一些演出者對於音樂風格的表演標準。 • 到目前為止，我們已進行這個計劃作為一個純粹的科學，基礎研究，然而在未來實際可能的應用和開發的結果也將予以研究。

  22. 感想： • 在使用machine learning & data mining 之前的前置作業要做好，這樣才可以得到比較好的實驗結果。

  23. The End

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