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An approach to automatic music playlist generation using iTunes and behavioral data

An approach to automatic music playlist generation using iTunes and behavioral data. By Darrius Serrant, Undergraduate Supervised by Mitsunori Ogihara, PhD CSC410: Computer Science Project Planning . At a Glance. Motivation Automatic Playlist Generation Problem Related Work

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An approach to automatic music playlist generation using iTunes and behavioral data

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  1. An approach to automatic music playlist generation using iTunes and behavioral data By Darrius Serrant, Undergraduate Supervised by Mitsunori Ogihara, PhD CSC410: Computer Science Project Planning

  2. At a Glance • Motivation • Automatic Playlist Generation Problem • Related Work • Scope of Project • System Features • Process Overview • Testing and Evaluation

  3. Motivation • Music: food for the soul! • Smorgasbord of expressions, emotions, and representations • Binds us to friends, memories, experiences, etc… • Marketable, available and consumable • The typical music library • 1,000+ titles • Diverse in features • Difficult to organize, explore, and experience

  4. Automatic Playlist Generation Problem • Manual playlist creation • Burdensome and time consuming • Subjective • Automatic playlist creation: • Create music playlists fulfilling arbitrary requirements • # of titles • Permutation • Measure of variety • An NP-hard problem

  5. Related Work • Scalable search algorithms1 • Search algorithms based on skipping behavior2 • Reduction to the traveling salesman problem3 • Local search CSP algorithm4 • Case-base approach to playlist generation5 • Song selection via a network flow model6 • The Music Genome Project7

  6. Related Work (continued) • Commonalities: • Assumes limited knowledge of music library • Assumes usage of audio feature extraction techniques • Requires explicit specification of playlist constraints

  7. Scope of Project • A unique approach to the automatic playlist generation problem • Eliminates explicit user specifications • Adapts to users’ listening preferences • More expressive than audio features extraction • Research objectives • Analyze contents of users’ music library • Monitor and learn users’ listening habits • Generate playlists of twelve songs by request

  8. System Features • iTunes Library Data Extraction • Extract music titles and their characteristics • Song Characteristics Aggregator • Collect metadata from Internet sources • Machine Learning • Statistically model users’ music listening habits • Playlist Generation • Build a playlist from a “playlist” state space

  9. System Features (continued) • User Feedback • Evaluation of generated playlists • Periodical mood assessments • Software application monitoring

  10. Process Overview

  11. Process Overview • User listens to music through iTunes • Monitor systems’ active processes • Monitor local weather forecasts • Receive user’s mood updates • User closes down iTunes • Begin pre-playlist generation tasks • Collect data from user’s iTunes Music Library • Collect data from Internet sources • Update user’s listening pattern

  12. Process Overview (continued) • Automatically generate a new playlist • Extract search heuristics from listening pattern. • Build a new playlist from the search space. • User evaluates the generated playlist • Incorporate user feedback into listening pattern

  13. Testing and Evaluation • Phase One: Theoretical Testing • Under simulated conditions • Tasks: • Evaluate scalability of search algorithms • Verify production of desired playlists for “naïve” users • Phase Two: Live Testing • Deliver product to actual users • Tasks: • Evaluate scalability of search algorithms for Mac and PC users • Verify production of desired playlists for “actual” users • Test effects of volatile mood and environmental changes on playlist generation.

  14. Current and Future Work • Version 1.0 in development • iTunes Data Extractor • Apache Xerces 2.7 XML Parser • Data Collectors • Mood Collection • System Process Collection • Listening Pattern Assembly • Machine Learning • Weka 3.6 Supervised Learning Algorithms • Decision Tree Learning • Search Algorithms • Breadth-first search • Local beam search • Genetic algorithm

  15. Current and Future Work (continued) • Version 1.0 in development (continued) • Data Storage • Oracle Berkeley DB Java Edition • Testing • Theoretical testing • Evaluation of developed search algorithms • Future Work • International Symposium on Music Information Retrieval • The complete concept

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