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ACE: A Framework for optimizing music classification

ACE: A Framework for optimizing music classification. Cory McKay Rebecca Fiebrink Daniel McEnnis Beinan Li Ichiro Fujinaga Music Technology Area Faculty of Music McGill University. Goals. Highlight limitations of existing pattern recognition software when applied to MIR

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ACE: A Framework for optimizing music classification

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  1. ACE: A Framework for optimizing music classification Cory McKay Rebecca Fiebrink Daniel McEnnis Beinan Li Ichiro Fujinaga Music Technology Area Faculty of Music McGill University

  2. Goals • Highlight limitations of existing pattern recognition software when applied to MIR • Present solutions to these limitations • Stress importance of standardized classification and feature extraction software • Ease of use, portability and extensibility • Present the ACE software framework • Uses meta-learning • Uses classification ensembles 2/25

  3. Existing music classification systems • Systems often implemented with specific tasks in mind • Not extensible to general tasks • Often difficult to use for those not involved in project • Need standardized systems for a variety of MIR problems • No need to reimplement existing algorithms • More reliable code • More usable software • Facilitates comparison of methodologies • Important foundations • Marsyas (Tzanetakis & Cook 1999) • M2K (Downie 2004) 3/25

  4. Existing general classification systems • Available general-purpose systems: • PRTools (van der Heijden et al. 2004 ) • Weka (Witten & Frank 2005) • Other meta-learning systems: • AST (Lindner and Studer 1999) • Metal (www.metal-kdd.org) 4/25

  5. Problems with existing systems • Distribution problems • Proprietary software • Not open source • Limited licence • Music-specific systems are often limited • None use meta-learning • Classifier ensembles rarely used • Interfaces not oriented towards end users • General-purpose systems not designed to meet the particular needs of music 5/25

  6. Special needs of music classification (1) • Assign multiple classes to individual recordings • A recording may belong to multiple genres, for example • Allow classification of sub-sections and of overall recordings • Audio features often windowed • Useful for segmentation problems • Maintain logical grouping of multi-dimensional features • Musical features often consist of vectors (e.g. MFCC’s) • This relatedness can provide classification opportunities 6/25

  7. Special needs of music classification (2) • Maintain identifying meta-data about instances • Title, performer, composer, date, etc. • Take advantage of hierarchically structured taxonomies • Humans often organize music hierarchically • Can provide classification opportunities • Interface for any user 7/25

  8. Standardized file formats • Existing formats such as Weka’s ARFF format cannot represent needed information • Important to enable classification systems to communicate with arbitrary feature extractors • Four XML file formats that meet the above needs are described in proceedings 8/25

  9. The ACE framework • ACE (Autonomous Classification Engine) is a classification framework that can be applied to arbitrary types of music classification • Meets all requirements presented above • Java implementation makes ACE portable and easy to install 9/25

  10. ACE and meta-learning • Many classification methodologies available • Each have different strengths and weaknesses • Uses meta-learning to experiment with a variety of approaches • Finds approaches well suited to each problem • Makes powerful pattern recognition tools available to non-experts • Useful for benchmarking new classifiers and features 10/25

  11. Feature Extraction System Model Classifications Extracted Features Feature Settings Taxonomy Music Recordings Trained Classifiers Statistical Comparison of Classification Methodologies Experiment Coordinator Classification Methodology 1 Classification Methodology n ACE Dimensionality Reduction Dimensionality Reduction … Classifier Evaluator 11/25

  12. Algorithms used by ACE • Uses Weka class libraries • Makes it easy to add or develop new algorithms • Candidate classifiers • Induction trees, naive Bayes, k-nearest neighbour, neural networks, support vector machines • Classifier parameters are also varied automatically • Dimensionality reduction • Feature selection using genetic algorithms, principal component analysis, exhaustive searches • Classifier ensembles • Bagging, boosting 12/25

  13. Classifier ensembles • Multiple classifiers operating together to arrive at final classifications • e.g. AdaBoost (Freund and Shapire 1996) • Success rates in many MIR areas are behaving asymptotically (Aucouturier and Pachet 2004) • Classifier ensembles could provide some improvement 13/25

  14. Musical evaluation experiments • Achieved a 95.6% success with a five-class beatbox recognition experiment (Sinyor et al. 2005) • Repeated Tindale’s percussion recognition experiment (2004) • ACE achieved 96.3% success, as compared to Tindale’s best rate of 94.9% • A reduction in error rate of 27.5% 14/25

  15. General evaluation experiments • Applied ACE to six commonly used UCI datasets • Compared results to recently published algorithm (Kotsiantis and Pintelas 2004) 15/25

  16. Results of UCI experiments (1) 16/25

  17. Results of UCI experiments (2) • ACE performed very well • Statistical uncertainty makes it difficult to say that ACE’s results are inherently superior • ACE can perform at least as well as a state of the art algorithm with no tweaking • ACE achieved these results using only one minute per learning scheme for training and testing 17/25

  18. Results of UCI experiments (3) • Different classifiers performed better on different datasets • Supports ACE’s experimental meta-learning approach • Effectiveness of AdaBoost (chosen 2 times out of 6) demonstrates strength of classifier ensembles 18/25

  19. Feature extraction • ACE not tied to any particular feature extraction system • Reads Weka ARFF as well as ACE XML files • Does include two powerful and extensible feature extractors are bundled with ACE • Write Weka ARFF as well as ACE XML 19/25

  20. jAudio • Reads: • .mp3 • .wav • .aiff • .au • .snd 20/25

  21. jSymbolic • Reads MIDI • Uses 111 Bodhidharma features 21/25

  22. Graphical interface Includes an on-line manual Command-line interface Batch processing External calls Java API Open source Well documented Easy to extend ACE’s interface 22/25

  23. Current status of ACE • In alpha release • Full release scheduled for January 2006 • Finalization of GUI • User constraints on training, classification and meta-learning times • Feature weighting • Expansion of candidate algorithms • Long-term • Distributed processing, unsupervised learning, blackboard systems, automatic cross-project optimization 23/25

  24. Conclusions • Need standardized classification software able to deal with the special needs of music • Techniques such as meta-learning and classifier ensembles can lead to improved performance • ACE designed to address these issues 24/25

  25. Web site: • coltrane.music.mcgill.ca/ACE • E-mail: • cory.mckay@mail.mcgill.ca

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