Jsymbolic
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jSymbolic. Jordan Smith – MUMT 611 – 6 March 2008. Overview. jSymbolic extracts high-level features from symbolic (MIDI) data. Walkthrough of the interface Features: Types of features Motivation for choice of features Extraction Planned improvements. Overview.

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Jsymbolic

jSymbolic

Jordan Smith – MUMT 611 – 6 March 2008


Overview

Overview

jSymbolic extracts high-level features from symbolic (MIDI) data.

  • Walkthrough of the interface

  • Features:

    • Types of features

    • Motivation for choice of features

    • Extraction

  • Planned improvements


Overview1

Overview

jSymbolic extracts high-level features from symbolic (MIDI) data.

  • Walkthrough of the interface

  • Features:

    • Types of features

    • Motivation for choice of features

    • Extraction

  • Planned improvements


Features

Features

  • 3 kinds of features:

    • Low-level

    • High-level

    • Cultural


Features1

Features

  • 7 categories of high-level features:

    • Instrumentation (20)

    • Texture (20)

    • Rhythm (35)

    • Dynamics (4)

    • Pitch statistics (26)

    • Melody (20)

    • Chords (28)


Features2

Features

  • Why so many features?

    • Ensure ability to discriminate as many different kinds of music as possible

    • Want features to be as basic as possible, because:

      • They are destined for a machine learning experiment

      • Estimating complex features is controversial


Features3

Features

  • Why pick these features?

    • Long history of musicological interest

    • Relative ease of extraction


Features4

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.)


Jsymbolic

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


Overview2

Overview

jSymbolic extracts high-level features from symbolic (MIDI) data.

  • Walkthrough of the interface

  • Features:

    • Types of features

    • Motivation for choice of features

    • Extraction

  • Planned improvements


Using the features

Using the Features

  • 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


Feature extraction

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.

  • Use MIDI Toolbox.


Midi toolbox vs jsymbolic

MIDI Toolbox vs. jSymbolic

  • jSymbolic

    -requires JAVA

    -is strictly for extracting features

    -analytical goals: usefully and objectively condense information

  • Toolbox

    -requires MATLAB

    -has tools for manipulating and visualizing data

    -analytical goals: estimate a musicologically important feature


Planned improvements

Planned Improvements

  • Boost number of features from 111 to 160

  • Ability to operate on non-MIDI symbolic data (MusicXML, GUIDO, kern)

  • Ability to extract over windows


Jsymbolic

Questions


References

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.

    Details of features implemented in jSymbolic:

  • 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.

    (This study used a previous version of jSymbolic called Bodhidharma.)


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