1. 1 / 22 jSymbolic Jordan Smith – MUMT 611 – 6 March 2008

2. 2 / 22 Overview jSymbolic extracts high-level features from symbolic (MIDI) data. Walkthrough of the interface Walkthrough of the interface Features: Features: Types of features Types of features Motivation for choice of features Motivation for choice of features Extraction Extraction Planned improvements Planned improvements

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9. 9 / 22 Overview jSymbolic extracts high-level features from symbolic (MIDI) data. Walkthrough of the interface Walkthrough of the interface Features: Features: Types of features Types of features Motivation for choice of features Motivation for choice of features Extraction Extraction Planned improvements Planned improvements

10. 10 / 22 Features 3 kinds of features: 3 kinds of features: Low-level Low-level High-level High-level Cultural Cultural

11. 11 / 22 Features 7 categories of high-level features: 7 categories of high-level features: Instrumentation (20) Instrumentation (20) Texture (20) Texture (20) Rhythm (35) Rhythm (35) Dynamics (4) Dynamics (4) Pitch statistics (26) Pitch statistics (26) Melody (20) Melody (20) Chords (28) Chords (28)

12. 12 / 22 Features Why so many features? Why so many features? Ensure ability to discriminate as many different kinds of music as possible Ensure ability to discriminate as many different kinds of music as possible Want features to be as basic as possible, because: Want features to be as basic as possible, because: They are destined for a machine learning experiment They are destined for a machine learning experiment Estimating complex features is controversial Estimating complex features is controversial

13. 13 / 22 Features Why pick these features? Why pick these features? Long history of musicological interest Long history of musicological interest Relative ease of extraction Relative ease of extraction

14. 14 / 22 Features Why pick these features? Why pick these features? “The features described above have been designed according to those used in musicological studies, but there is no theoretical support for their … characterization capability.” (Ponce de León. 2004. Statistical Description Models for Melody Analysis and Characterization. ICMC Proceedings 149-56.)

15. 15 / 22 McKay & Fujinaga 2005: Automatic music classification and the importance of instrument identification. Proceedings of the Conference on Interdisciplinary Musicology.

16. 16 / 22 Overview jSymbolic extracts high-level features from symbolic (MIDI) data. Walkthrough of the interface Walkthrough of the interface Features: Features: Types of features Types of features Motivation for choice of features Motivation for choice of features Extraction Extraction Planned improvements Planned improvements

17. 17 / 22 Using the Features Like jAudio, modular features make it easy to add new ones Like jAudio, modular features make it easy to add new ones -- ADDING FEATURES -- Implement a class for the new feature in the jAudioFeatureExtractor/MIDIFeatures directory. It must extend the MIDIFeatureExtractor abstract class. Add a reference to the new class to the populateFeatureExtractors method in the SymbolicFeatureSelectorPanel class. Features exported to ACE XML or Weka ARFF Features exported to ACE XML or Weka ARFF

18. 18 / 22 Feature Extraction Other than jSymbolic, what is the state of the art in symbolic feature extraction? Other than jSymbolic, what is the state of the art in symbolic feature extraction? Borrow from others or invent your own, and implement them by yourself. Borrow from others or invent your own, and implement them by yourself. Use MIDI Toolbox. Use MIDI Toolbox.

19. 19 / 22 MIDI Toolbox vs. jSymbolic Toolbox Toolbox -requires MATLAB -has tools for manipulating and visualizing data -analytical goals: estimate a musicologically important feature jSymbolic jSymbolic -requires JAVA -is strictly for extracting features -analytical goals: usefully and objectively condense information

20. 20 / 22 Planned Improvements Boost number of features from 111 to 160 Boost number of features from 111 to 160 Ability to operate on non-MIDI symbolic data (MusicXML, GUIDO, kern) Ability to operate on non-MIDI symbolic data (MusicXML, GUIDO, kern) Ability to extract over windows Ability to extract over windows

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22. 22 / 22 References jSymbolic overview: McKay, C., and I. Fujinaga. 2006. jSymbolic: A feature extractor for MIDI files. Proceedings of the International Computer Music Conference. 302-5. McKay, C., and I. Fujinaga. 2006. jSymbolic: A feature extractor for MIDI files. Proceedings of the International Computer Music Conference. 302-5. Details of features implemented in jSymbolic: McKay, C. 2004. Automatic genre classification of MIDI recordings. (M.A. Thesis, McGill University). McKay, C. 2004. Automatic genre classification of MIDI recordings. (M.A. Thesis, McGill University). Example of jSymbolic’s feature extraction in action: McKay, C., and I. Fujinaga. 2005. Automatic music classification and the importance of instrument identification. Proceedings of the Conference on Interdisciplinary Musicology. McKay, C., and I. Fujinaga. 2005. Automatic music classification and the importance of instrument identification. Proceedings of the Conference on Interdisciplinary Musicology. (This study used a previous version of jSymbolic called Bodhidharma.)