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.
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
Jordan Smith – MUMT 611 – 6 March 2008
“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.)
-- 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.
-is strictly for extracting features
-analytical goals: usefully and objectively condense information
-has tools for manipulating and visualizing data
-analytical goals: estimate a musicologically important feature
Details of features implemented in jSymbolic:
Example of jSymbolic’s feature extraction in action:
(This study used a previous version of jSymbolic called Bodhidharma.)