Sequential decision-making in musical intelligence:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham, Switzerland
Springer
[2020]
|
| Schriftenreihe: | Studies in computational intelligence
volume 857 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xxv, 206 Seiten Illustrationen, Diagramme |
| ISBN: | 9783030305185 9783030305215 |
Internformat
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Datensatz im Suchindex
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| adam_text | Contents 1 Introduction.............................................................................................. 1 1.1 Research Question and Contributions................................................ 1.2 Book Structure..................................................................................... References........................................................................................................ 2 5 8 2 Background............... 9 2.1 Reinforcement Learning..................................................................... 2.2 AI, Music and Agents......................................................................... 2.3 Music and Human Behavior.............................................................. 2.4 Summary............................................................................................... References........................................................................................................ 9 11 12 12 13 3 Playlist Recommendation....................................................................... 15 3.1 3.2 3.3 3.4 3.5 3.6 Music Playlist Recommendation as an MDP................................. Modeling............................................................................................... 3.2.1 Modeling Songs....................................................................... 3.2.2 Modeling the Listener Reward Function............................ 3.2.3 Expressiveness of the Listener Model.................................
Data........................................................................................................ DJ-MC................................................................................................... 3.4.1 Learning Initial Song Preferences........................................ 3.4.2 Learning Initial Transition Preferences............................... 3.4.3 Learning on the Fly................................................................ 3.4.4 Planning................................................................................... Evaluation in Simulation.................................................................... 3.5.1 Performance of DJ-MC with Feature Dependent Listeners.................................................................................. 3.5.2 A Feature-Dependent DJ-MC............................................... Evaluation on Human Listeners......................................................... 3.6.1 Experimental Setup................................................................ 16 18 18 19 20 21 23 23 24 25 26 27 30 30 33 34
x 4 5 Contents 3.7 Planning Extensions to DJ-MC......................................................... 3.7.1 Upper Confidence Bound in Trees (UCT).......................... 3.8 UCT for Playlist Generation............................................................. 3.9 Planning for Personalization............................................................. 3.10 Planning for Diversity......................................................................... 3.11 Summary.............................................................................................. References........................................................................................................ 37 37 38 40 40 43 43 Algorithms for TrackingChanges in PreferenceDistributions............ 4.1 Model Retraining as a Markov Decision Process.......................... 4.1.1 Markov Decision Processes.................................................. 4.1.2 Formulation of the Model Retraining Problem................... 4.2 Learning a Policy through Approximate Value Iteration.............. 4.3 Theoretical Intuition............................................................................ 4.4 Distribution Model Retraining........................................................... 4.4.1 MDP Representation for the Distribution Tracking Problem................................................................................... 4.4.2 AVI for Distribution Model Retraining............................... 4.5 Distribution Tracking—Empirical Evaluation................................. 4.5.1 Proof of
Concept—Synthetic Data...................................... 4.5.2 Real World Domain I—ThisIsMyJam Dataset................... 4.5.3 Real World Domain Π—ECOMP........................................ 4.6 Prediction Model Retraining.............................................................. 4.6.1 MDP Representation for the Prediction Problem.............. 4.6.2 Real World Domain—ECOMP............................................. 4.7 Scaling Up Model Retraining with Fitted Value Iteration and Deep Neural Nets......................................................................... 4.7.1 Fitted Value Iteration............................................................. 4.7.2 Generalizing the Model Retraining Framework with Neural Fitted Value Iteration........................................ 4.7.3 Empirical Evaluation.......................................... 4.8 Summary and Discussion.................................................................. References ........................................................................................................ 47 49 50 50 52 53 53 Modeling theImpact of Music on HumanDecision-Making................. 5.1 The Drift-Diffusion Model.................................................................. 5.2 First Experiment: Impact on Emotional Classification................... 5.3 Methods of the First Experiment...................................................... 5.4 Results of the First Experiment......................................................... 5.5 Correlating Responses and Musical Features in the Context of Emotional
Classification................................................................ 5.5.1 Extracting Raw Auditory Features...................................... 5.5.2 Processing Participant Responses........................................ 5.5.3 Observed Correlations........................................................... 54 54 56 56 58 58 60 60 61 62 62 63 63 64 65 67 68 70 71 72 75 75 75 76
Contents Second Experiment: Impact on Quantitative Decision-Making................................................................................. 5.7 Methods of the Second Experiment.................................................. 5.8 Results of the Second Experiment.................................................... 5.9 Correlating Responses and Musical Features in the Context of Gambling Behavior......................................................................... 5.9.1 Extracting Raw Auditory Features ...................................... 5.9.2 Processing Observed Gambling Behavior.......................... 5.9.3 Observed Correlations........................................................... 5.10 Summary and Discussion.................................................................. References........................................................................................................ xi 5.6 6 Impact of Music on Person-Agent Interaction...................................... 6.1 First Experiment—The Impact of Music on Cooperative Task Behavior..................................................................................... 6.1.1 Procedure................................................................................ 6.1.2 Participants.............................................................................. 6.1.3 Autonomous Car Behavior.................................................... 6.1.4 Music........................................................................................ 6.2 Overview of Results for the First
Experiment................................. 6.2.1 Minimal Distance from Autonomous Car.......................... 6.2.2 Driving Speed......................................................................... 6.2.3 Right of Way......................................................................... 6.3 Breakdown of User Behavior Under Different Trial Conditions in the First Experiment .............................................. 6.3.1 Behavior Under Different Autonomous Car Intersection Wait Times......................................................... 6.3.2 Behavior Under Different Autonomous Car Average Speed....................................................................... 6.4 Impact of Musical Parameters on User Behavior in the First Experiment....................................................................... 6.4.1 Extracting Raw Auditory Features ...................................... 6.4.2 Results..................................................................................... 6.4.3 Loudness and Overall Time Out of Intersection................. 6.4.4 Loudness, Speed, Time Stopped, and Minimal Distance................................................................................... 6.4.5 Tempo and Hesitancy........................................................... 6.4.6 Additional Observations........................................................ 6.5 Second Experiment—Introducing aLearning Agent....................... 6.5.1 Procedure................................................................................ 6.5.2
Participants.............................................................................. 6.5.3 Music....................................................................................... 6.5.4 Autonomous Vehicle Behavior and Learning Architecture.............................................................................. 78 79 80 83 83 83 83 86 87 89 90 90 90 92 92 92 93 93 94 95 95 95 95 96 97 97 98 99 100 100 100 101 101 101
Contents xii 6.6 Results of the Second Experiment.................................................... 6.6.1 Establishing the Impact of Music on Human Behavior................................................................................... 6.6.2 Impact on Average Autonomous Agent Completion Time.......................................................................................... 6.6.3 Impact on the Crash Rate...................................................... 6.7 Summary and Discussion.................................................................. References........................................................................................................ 7 8 104 104 105 106 108 109 MultiagentCollaborationLearning: AMusic Generation TestCase........................................................................................................ 7.1 Background.......................................................................................... 7.1.1 Reinforcement Learning......................................................... 7.1.2 Multiagent Reinforcement Learning................................... 7.2 Methods................................................................................................. 7.2.1 Learned Preferences................................................................ 7.2.2 Behavioral Cloning................................................................ 7.2.3 Generative Adversarial Imitation Learning.......................... 7.3 Game-Theoretic Analysis.................................................................. 7.4
Experiments: Multiagent Music Generation................................... 7.4.1 Domain Description................................................................ 7.4.2 Reward Function.................................................................... 7.4.3 Generating Examples for Behavioral Cloning................... 7.4.4 Learning Architecture............................................................. 7.5 Results in the Music Generation Domain........................................ 7.6 Extending the Results to a Wider Range of Agent and Preference Mixtures.................................................................... 7.6.1 Agent Types............................................................................ 7.6.2 Reward Mixing Configurations............................................. 7.6.3 Analysis................................................................................... 7.7 Experiments with Fixed Agents......................................................... 7.8 Extending the Results to a Second Domain: Predator-Prey......... 7.8.1 Preference Models.................................................................. 7.8.2 Learning Architecture............................................................. 7.9 Results in the Predator-Prey Domain............................................... 7.10 Summary and Discussion.................................................................. References........................................................................................................ 124 125 126 127 129 132 135 136 137 137 140 Related
Work anda Taxonomyof MusicalIntelligence Tasks............. 8.1 Background and Motivation............................................................. 8.2 A Taxonomy of Music AI Problems............................................... 8.2.1 Partition by the Nature of the Task...................................... 8.2.2 Partition by Input Type......................................................... 8.2.3 Partition by Algorithmic Technique.................................... 143 146 148 149 150 150 Ill 112 113 113 114 115 115 116 117 119 119 120 121 122 122
Contents xiii 8.3 Overview of Musical Tasks............................................................ 8.3.1 Classification and Identification Tasks............................... 8.3.2 Retrieval Tasks................................................................... 8.3.3 Musical Skill Acquisition Tasks........................................ 8.3.4 Generation Tasks................................................................. 8.4 Overview of Common Representations ........................................ 8.4.1 Symbolic Representations for Music.................................. 8.4.2 Audio Representations and Derived Features.................... 8.5 Overview of Technique................................................................... 8.5.1 Machine Learning Approaches.......................................... 8.5.2 Formal Methods ................................................................. 8.5.3 Agent-Based Techniques................................................... 8.6 Evaluation Methods for Music AI................................................. 8.6.1 Evaluation of Classification Tasks...................................... 8.6.2 Evaluation of Skill Acquisition Tasks............................... 8.6.3 Evaluation of Retrieval Tasks............................................. 8.6.4 Qualitative Evaluation........................................................ 8.7 Summary and Discussion: Open Problems.................................... References.................................................................................................. 151 152 155
158 166 168 168 170 172 172 177 177 179 179 180 180 181 181 183 Conclusion and Future Work..................................................................... 197 9.1 9.2 198 199 200 200 9 Book Contributions......................................................................... Future Work................................................................................ .. · 9.2.1 Playlist Recommendation................................................... 9.2.2 Adaptive Model Management............................................. 9.3 Modeling the Impact of Music on Human Behavior and Improving Agent-Person Interaction with Music Stimuli.............................................................................................. 9.4 Multiagent Cooperation in the Face of Preexisting Preferences....................................................................................... 9.5 Concluding Remarks....................................................................... References.................................................................................................. Appendix: Glossary 201 202 203 203 205
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| spelling | Liebman, Elad Verfasser (DE-588)1026016177 aut Sequential decision-making in musical intelligence Elad Liebman Cham, Switzerland Springer [2020] © 2020 xxv, 206 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Studies in computational intelligence volume 857 Dissertation UT Austin Sequentialanalyse (DE-588)4128461-6 gnd rswk-swf Musikalität (DE-588)4040837-1 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Entscheidungsfindung (DE-588)4113446-1 gnd rswk-swf Musikalischer Geschmack (DE-588)4170805-2 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Musikalischer Geschmack (DE-588)4170805-2 s Musikalität (DE-588)4040837-1 s Entscheidungsfindung (DE-588)4113446-1 s Sequentialanalyse (DE-588)4128461-6 s Künstliche Intelligenz (DE-588)4033447-8 s DE-604 Erscheint auch als Online-Ausgabe 978-3-030-30519-2 Studies in computational intelligence volume 857 (DE-604)BV020822171 857 Digitalisierung BSB München - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031599081&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| title | Sequential decision-making in musical intelligence |
| title_auth | Sequential decision-making in musical intelligence |
| title_exact_search | Sequential decision-making in musical intelligence |
| title_full | Sequential decision-making in musical intelligence Elad Liebman |
| title_fullStr | Sequential decision-making in musical intelligence Elad Liebman |
| title_full_unstemmed | Sequential decision-making in musical intelligence Elad Liebman |
| title_short | Sequential decision-making in musical intelligence |
| title_sort | sequential decision making in musical intelligence |
| topic | Sequentialanalyse (DE-588)4128461-6 gnd Musikalität (DE-588)4040837-1 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Entscheidungsfindung (DE-588)4113446-1 gnd Musikalischer Geschmack (DE-588)4170805-2 gnd |
| topic_facet | Sequentialanalyse Musikalität Künstliche Intelligenz Entscheidungsfindung Musikalischer Geschmack Hochschulschrift |
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