1. A N APPROACH TO AUTOMATIC MUSIC PLAYLIST GENERATION USING I T UNES AND BEHAVIORAL DATA By Darrius Serrant, Undergraduate Supervised by Mitsunori Ogihara, PhD CSC410: Computer Science Project Planning

2. A T A G LANCE Motivation Automatic Playlist Generation Problem Related Work Scope of Project System Features Process Overview Testing and Evaluation

3. M OTIVATION 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. A UTOMATIC P LAYLIST G ENERATION P ROBLEM 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. R ELATED W ORK Scalable search algorithms 1 Search algorithms based on skipping behavior 2 Reduction to the traveling salesman problem 3 Local search CSP algorithm 4 Case-base approach to playlist generation 5 Song selection via a network flow model 6 The Music Genome Project 7

6. R ELATED W ORK ( CONTINUED ) Commonalities: Assumes limited knowledge of music library Assumes usage of audio feature extraction techniques Requires explicit specification of playlist constraints

7. S COPE OF P ROJECT 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. S YSTEM F EATURES 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. S YSTEM F EATURES ( CONTINUED ) User Feedback Evaluation of generated playlists Periodical mood assessments Software application monitoring

10. P ROCESS O VERVIEW

11. 1. User listens to music through iTunes 1. Monitor systems’ active processes 2. Monitor local weather forecasts 3. Receive user’s mood updates 2. User closes down iTunes 3. Begin pre-playlist generation tasks 1. Collect data from user’s iTunes Music Library 2. Collect data from Internet sources 3. Update user’s listening pattern

12. P ROCESS O VERVIEW ( CONTINUED ) 4. Automatically generate a new playlist 1. Extract search heuristics from listening pattern. 2. Build a new playlist from the search space. 5. User evaluates the generated playlist 6. Incorporate user feedback into listening pattern

13. T ESTING AND E VALUATION 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. C URRENT AND F UTURE W ORK 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. C URRENT AND F UTURE W ORK ( 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