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How to Think Music with Data Translating from Audio Content Analysis to Music Analysis Andersen, Jesper Steen

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How to Think Music with Data Translating from Audio Content Analysis to Music Analysis Andersen, Jesper Steen How to Think Music with Data Translating from Audio Content Analysis to Music Analysis Andersen, Jesper Steen Publication date: 2017 Document version Peer reviewed version Citation for published version (APA): Andersen, J. S. (2017). How to Think Music with Data: Translating from Audio Content Analysis to Music Analysis. Det Humanistiske Fakultet, Københavns Universitet. Download date: 05. Oct. 2021 HOW TO THINK MUSIC WITH DATA Translating from Audio Content Analysis to Music Analysis JESPER STEEN ANDERSEN 1 Title: How to Think Music with Data Subtitle: Translating from Audio Content Analysis to Music Analysis Thesis submitted to the PhD school at University of Copenhagen, Royal School of Library and Information Science, June 23th 2017 Author: Jesper Steen Andersen Word count: 67.670 excl. bibliography and appendices Supervisors: Jack Andersen, University of Copenhagen, Denmark Morten Michelsen, University of Copenhagen, Denmark Evaluation committee: Jeppe Nicolajsen, University of Copenhagen, Denmark Iben Have, AU, Aarhus University, Denmark Alan Marsden, Lancaster University, UK 2 Contents Links to Data ....................................................................................................................... 5 1. Introduction .................................................................................................................... 7 1.1 Situating the Project Part 1: Big Data in the Humanities ...................................................................................... 7 1.2 Situating the Project Part 2: The CoSound Project ............................................................................................... 12 1.3 My Intentions .................................................................................................................................................................. 12 1.4 Premises ........................................................................................................................................................................... 14 1.5 On Writing Style ............................................................................................................................................................ 17 1.6 Content of thesis ............................................................................................................................................................ 17 1.7 Terminology ..................................................................................................................................................................... 19 1.8 Rounding off Chapter 1 ............................................................................................................................................... 22 2. Related Research ......................................................................................................... 23 2.1. Research on MIR and data analysis ........................................................................................................................ 24 2.2. Related Musicological Research ................................................................................................................................ 27 2.3 Related Digital Humanities Research ...................................................................................................................... 36 2.4 Rounding off Chapter 2 ............................................................................................................................................... 40 3. Focus Points Where to Focus and How to Focus ................................................... 41 3.1 MIR and Musicology - Different End Goals ............................................................................................................ 41 3.2 Concern #1: How to Use Quantitative Methods in a Qualitative Discipline? ............................................... 43 3.3 Concern #2: How to Interpret MIR Features? ...................................................................................................... 47 3.4 How to Solve These Concerns .................................................................................................................................... 50 3.5 Choice of Analyses ......................................................................................................................................................... 52 3.6 Rounding off Chapter 3 ............................................................................................................................................... 53 4. From Audio Content Analysis to Music Analysis ................................................... 55 4.1. Data Creation with ACA ............................................................................................................................................. 55 4.2 The Advantages .............................................................................................................................................................. 59 4.3 How Humanities Objectives Fit with Data Approaches ...................................................................................... 65 4.4 But What to Learn about Music with ACA Methods? ......................................................................................... 69 4.5 Can We Trust Data? .................................................................................................................................................... 78 4.6 Can We Trust the Analysis? ........................................................................................................................................ 81 4.7 Can We Trust Data-Driven Approaches? ............................................................................................................... 84 4.8 Rounding off Chapter 4 ............................................................................................................................................... 91 5. Echo Nest’s Features Bridging from Machine Learning to Musicology ............. 92 5.1 Introduction to Machine-learned Features .............................................................................................................. 92 5.2. On Echo Nest and My Purpose ................................................................................................................................ 94 5.3 The Features ................................................................................................................................................................... 96 5.4 Introduction to the Values - the Basics .................................................................................................................... 99 5.5 Epistemological Value – How to Interpret ............................................................................................................ 103 5.6 Practival Value - The Usefulness of Reductions ................................................................................................... 110 5.7 Perspectives for Musicology ...................................................................................................................................... 116 5.8 Rounding off Chapter 5 ............................................................................................................................................. 120 3 6. Then the Science Guys Entered the Room .......................................................... 121 6.1 The Analysis .................................................................................................................................................................. 121 6.2 My Purpose ................................................................................................................................................................... 126 6.3 Challenging the Epistemological Claims ................................................................................................................ 126 6.4 My Interpretation of the Study ................................................................................................................................ 132 6.5 Prospects ........................................................................................................................................................................ 134 6.6 Conclusion ...................................................................................................................................................................... 138 7. A Corpus Study of 89 DJ Sets ................................................................................. 140 7.1 Introduction ................................................................................................................................................................... 141 7.2 Step 1 - Exploring the Features ............................................................................................................................... 145 7.3 Step 2 - Surface Views: Exploring the Datasets by Mapping Them .............................................................. 166 7.4 Step 3 - The Shape of the Set: Analysis on the Macro level ........................................................................... 176 7.5 Step 5 - Exploring Compositional Traits: Analysis on the Meso-level ............................................................ 181 7.6 Conclusion .....................................................................................................................................................................
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