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 .....................................................................................................................................................................