Similarity and Style in Electronic Dance Music Drum Rhythms Daniel Gómez Marín PHD THESIS UPF / 2018 THESIS DIRECTOR Dr. Sergi Jordà Music Technology Group Dept. of Information and Communication Technologies iii iv Acknowledgments This thesis is dedicated to Eulalia, Elisa and León, my beautiful family, who have always encouraged curiosity and endurance with great kindness. During this doctoral process I became very close to my PhD fellows Ángel Faraldo, Cárthach O'Nuánain and Martin Hermant as we became intimate friends, sharing the intensity of a PhD process. Together we won prizes, traveled, taught, discussed, struggled and laughed. Our team became a reality mostly by the sharpness of Dr. Sergi Jordà who decided it was a good idea to have us four as his PhD students with the co- supervision of Perfecto Herrera. I am very thankful to both Sergi and Perfe as their guides and challenges to my ideas traced the many roads that finally materialized as this thesis. I want to acknowledge everyone who kindly took their time to participate in the different listening experiments, and specially the musicians Sano and Boska who generously shared their talent and time to reflect on the systems that I worked on. During my life as a PhD student I was surrounded by marvelous people who made the process possible, and whom I also want to thank. Andrea who became my family and support. The guys from the Veni Vidi Bici bicycle club, with whom I explored so many kilometers of the beautiful roads in Cataluña. Nico, Cata, GC and Cosmo who introduced me to the calm of Montseny and the beauty of Sant Pol de Mar. All the MTG staff, specially Xavier Serra, Sebas Mealla, Marius Miron, David D'Almazzo and Alastair Porter. During the writing stage, Paula shed a bright light to process, reminding me the art of day-by-day construction and giving me her patient love. v Abstract This thesis presents original research carried out in the topic of electronic dance music (EDM) drum sequencing, a fundamental and yet underdeveloped subject in the music production literature. The work undertaken is focused in two main areas: similarity between drum patterns and modeling of drumming style. The study of pattern similarity is rooted in current knowledge on human processing of monophonic rhythms, and is expanded until a model capable of predicting similarity sensations of polyphonic drum rhythms is reached. With this model, RhythmSpace, a graphical system for the continuous real-time exploration of drum pattern collections, is developed. The second area of research, drumming style modeling, is approached from a statistical perspective, developing a generative model capable of learning styles from examples and creating original drum patterns in the learned styles. This model allows high-level musical flexibility, letting a musician combine and transform styles in real-time during the generative process. Taking advantage of this model, a style-based drum machine application, DrDrums, is implemented and evaluated in subject-based experiments. vii Resumen Esta tesis presenta una investigación original llevada a cabo en el área de la secuenciación de baterías de música electrónica de baile (EDM), un tema fundamental y al mismo tiempo poco desarrollado en la literatura de producción musical. El trabajo realizado se enfoca en dos áreas: la similitud entre patrones de batería y los estilos en la composición de patrones percusivos. El estudio de la similitud entre patrones se fundamenta en el conocimiento actual del procesamiento humano de patrones monofónicos, y es expandido hasta alcanzar un modelo capaz de predecir sensaciones de similitud en ritmos polifónicos. Con este modelo se ha creado RhythmSpace, un sistema gráfico para la exploración en tiempo real de colecciones de patrones de batería. La segunda área de investigación, el estilo de composición de baterías, es abordada desde una perspectiva estadística, desarrollando un modelo generativo capaz de aprender estilos desde ejemplos y luego crear patrones originales en los estilos aprendidos. Este modelo estadístico permite una flexibilidad musical de alto nivel, haciendo posible que un músico combine y transforme estilos en tiempo real durante el proceso generativo. Usando este modelo se implementa DrDrums, una máquina de ritmos con inteligencia de estilo, que es evaluada experimentalmente con sujetos. viii ix Contents Pàg. Abstract ix Resumen x 1. INTRODUCTION 1 1.1. Motivation 1 1.2 Context 2 1.3 Existing technologies for EDM drum production 11 1.4 Opportunities and challenges 13 1.5 Thesis Outline 14 2. STATE OF THE ART 17 2.1 Introduction 17 2.1.1 What is Rhythm? 18 2.2 Human Rhythm Processing 19 2.2.1 Pulse and meter 20 2.2.2 Syncopation 25 2.2.2.1 Monophonic syncopation 26 2.2.2.2 Polyphonic syncopation 28 2.3 Similarity in Percussive Rhythms 30 2.3.1 Notions of similarity 31 2.3.1.1 Dimensionality reduction techniques 36 2.3.2 Monophonic rhythmic similarity 37 2.3.3 Polyphonic rhythmic similarity 42 2.3.4 Rhythm spaces 44 2.4 Style in EDM 45 2.4.1 Definitions of style 45 2.4.2 How to study EDM styles 48 2.5 EDM Production and Drum Sequencing 50 2.6 Generative Music Sequencing 55 2.6.1 Generative drum sequencing 58 2.7 Conclusions 61 xi 3 TOWARDS METRICS OF RHYTHM SIMILARITY 65 3.1 Introduction 65 3.2 Experiment 1: Monophonic similarity and syncopation 69 3.2.1 Methods 73 3.2.1.1 Participants 73 3.2.1.2 Material 73 Objective Distance Metrics 73 Stimuli 76 3.2.1.3 Procedure 77 3.2.2 Results 78 3.2.3 Discussion 81 3.3 Using Beat-Induced Similarity Ratings to Define Similarity Metrics 84 3.3.1 Introduction 84 3.3.2 Expanded metrics for monophonic similarity 85 3.3.2.1 Syncopation and awareness distance (SAD) 86 3.3.2.2 Pattern coincidence and awareness metric (PAD) 87 3.3.2.3 Deducing the weights for PAD and SAD 87 3.3.2.4 Example to compute the metrics 89 3.3.4 Discussion and conclusions 90 3.4 Symbolic Descriptors for Polyphonic Drum Similarity 90 3.4.1 Symbolic drum pattern descriptors 93 3.4.1.1 Number of Instruments (NOI) 96 3.4.1.2 hisync, midsync and losync 96 3.4.1.3 Polysync 96 3.4.1.4 hiD, midD, loD 97 3.4.1.5 losyness, midsyness, hisyness 96 3.4.1.6 stepD 97 3.4.1.7 lowness, midness, hiness 97 3.5 Experiment 2: Objective Similarity in Alf Gabrielsson's Rhythm Spaces 97 3.5.1 Methods 99 3.5.1.1 Materials 99 3.5.1.2 procedure 100 3.5.2 Results and discussion 100 3.6 Experiment 3: Generalizing Polyphonic Descriptors 102 xii for EDM 3.6.1 Methods 103 3.6.1.1 participants 103 3.6.1.2 Material 103 3.6.1.3 Procedure 105 3.6.2 Results 105 3.6.2.1 From EDM to GE1 108 3.6.2.2 Relevant set of descriptors 109 3.6.3 Discussion 109 3.7 Conclusions 111 4. GENERATIVE TOOLS FOR EDM RHYTHM 115 4.1 Introduction 115 4.1.1 Fundamentals of DrDrums 116 4.1.2 Fundamentals of RhythmSpace 116 4.1.3 Technologies of DrDrums and RhythmSpace 118 4.2 Agnostic Density Transformer 118 4.2.1 Agnostic density transformation example 122 4.2.2 Considerations of real-time interaction with density 123 4.3 Drumming Style Extractor 125 4.3.1 Pre-processing the patterns 126 4.3.2 Analyzing the patterns 128 4.3.3 Generalizing stylistic music knowledge extraction 131 4.4 Drum Pattern Generator 133 4.4.1 Using style information to generate a pattern 134 4.4.2 Style interaction concepts 136 4.4.2.1 Style combinations 136 4.4.2.2 Commonness 138 4.4.2.2.1 Sigmoid commonness 139 4.4.2.2.2 Power commonness 140 4.4.2.3 Inducing variation 141 4.4.2.4 inducing variation by timbre grouping 145 4.4.3 The order effect 147 4.4.4 Zero frequency states 147 4.5 DrDrums 148 4.5.1 DrDrums' new style creation 150 4.5.2 Main controls 151 4.5.3 Generation of patterns in style 151 4.5.4 Style combination 152 xiii 4.5.5 Inducing variation 152 4.5.6 Density post processing 153 4.6 Quantitative Evaluation of DrDrums 154 4.6.1 Method 155 4.6.1.1 Participants 155 4.6.1.2 Material 156 4.6.1.3 Procedure 156 4.6.2 Results 157 4.7 Qualitative Evaluation of Dr Drums 158 4.7.1 Method 159 4.7.1.1 Participants 159 4.7.1.2 Material 159 4.7.1.3 Procedure 159 4.7.2 results 160 4.7.3 Summary and discussion 164 4.8 Rhythm Spaces 167 4.8.1 Requirements of a rhythm space 169 4.8.2 Rhythm interpolation 171 4.8.2.1 Step-by-step naive algorithm (STENA) 173 4.8.2.2 Step-by-step event to onsets (STEVO) algorithm 175 4.8.2.3 STEVO-D with fixed density profile 176 4.8.3 Interpolation algorithm discussion 177 4.9 RhythmSpace Application 179 4.10 Conclusions 182 5 CONCLUSIONS AND FUTURE WORK 185 5.1 Original Contributions 188 5.1.1 Algorithms 188 5.1.2 Corpora 189 5.1.3 Applications 189 5.1.4 Experimental methods 190 5.1.5 Software Libraries 190 5.2 Further Research 190 5.3 Closing Remarks 191 APPENDIX A: LIST OF PUBLICATIONS 193 APPENDIX B: GLOSSARY 195 BIBLIOGRAPHY 197 xiv xv xvi 1.