jSymbolic 2

Overview

jSymbolic is a software application intended for conducting research in the fields of music information retrieval (MIR), music theory and musicology. Its primary purpose is to extract statistical information from musical data stored symbolically in file formats such as MIDI or MEI. This statistical information is formulated as feature values, which may be fed directly into automatic classification systems, may be used to query large musical datasets, or may be used by musicologists and music theorists for conducting empirical musical research.

jSymbolic includes an easy-to-use GUI, and may also be used via the command lien. It also has an API facilitating programmatic use. The software can be used either with its excellent general-purpose default settings, or advanced users can use it under a variety of settings (saved in a special configuration settings file).

Like all jMIR components, jSymbolic is free and open-source, and is designed to be used directly for conducting research as well as as a platform for iteratively developing new features that can then be shared amongst researchers. As such, jSymbolic emphasizes extensibility, and includes a modular design that facilitates the implementation and incorporation of new features, as well as the automatic provision of all other feature values to each new feature and dynamic feature extraction scheduling that automatically resolves feature dependencies. jSymbolic is implemented in Java in order to maximize cross-platform utilization.

jSymbolic is also part of the SIMSSA (Single Interface for Music Score Searching and Analysis) project, and is integrated with the music stored on the associated Elvis database.

Features Extracted

jSymbolic is packaged with a library of 172 unique implemented features. Some of these are multidimensional, for a total of 1230 feature values.These features were developed through extensive analysis of publications in the fields of music theory, musicology and MIR. Most of these features had not previously been applied to MIR research, and many of them are entirely novel. The features can be loosely divided into the following seven categories:

Screen Shot

Manual

Many more details on jSongMiner are available in the jSymbolic Manual.

Related Publications

McKay, C., T. Tenaglia, J. Cumming, and I. Fujinaga. 2017. Using statistical feature extraction to distinguish the styles of different composers. Accepted for publication at the Medieval and Renaissance Music Conference.

McKay, C., T. Tenaglia, and I. Fujinaga. 2016. jSymbolic2: Extracting features from symbolic music representations. Extended Abstracts for the Late-Breaking Demo Session of the 17th International Society for Music Information Retrieval Conference.

McKay, C. 2010. Automatic music classification with jMIR. Ph.D. Thesis. McGill University, Canada.

McKay, C., J. A. Burgoyne, J. Hockman, J. B. L. Smith, G. Vigliensoni, and I. Fujinaga. 2010. Evaluating the genre classification performance of lyrical features relative to audio, symbolic and cultural features. Proceedings of the International Society for Music Information Retrieval Conference. 213–8.

McKay, C., and I. Fujinaga. 2010. Improving automatic music classification performance by extracting features from different types of data. Proceedings of the ACM SIGMM International Conference on Multimedia Information Retrieval. 257–66.

McKay, C., and I. Fujinaga. 2008. Combining features extracted from audio, symbolic and cultural sources. Proceedings of the International Conference on Music Information Retrieval. 597–602.

McKay, C., and I. Fujinaga. 2007. Style-independent computer-assisted exploratory analysis of large music collections. Journal of Interdisciplinary Music Studies 1 (1): 63–85.

McKay, C., and I. Fujinaga. 2006. jSymbolic: A feature extractor for MIDI files. Proceedings of the International Computer Music Conference. 302–5.

Questions and Comments

cory.mckay@mail.mcgill.ca

DOWNLOAD FROM SOURCEFORGE

-top of page-