jMIR is an open-source software suite implemented in Java for use in music information retrieval (MIR) research. It can be used to study music in the form of audio recordings, symbolic encodings and lyrical transcriptions, and can also mine cultural information from the Internet. It also includes tools for managing and profiling large music collections and for checking audio for production errors. jMIR includes software for extracting features, applying machine learning algorithms, applying heuristic error checkers, mining metadata and analyzing metadata.

The primary emphasis of jMIR is on providing software for general research in automatic music classification and similarity analysis. The main goals of the project are as follows:

• Make sophisticated pattern recognition technologies accessible to music researchers with both technical and non-technical backgrounds.
• Eliminate redundant duplication of effort.
• Increase cooperation and communication between research groups.
  • Facilitate iterative development and sharing of new MIR technologies.
  • Facilitate objective comparisons of algorithms.
• Facilitate research combining high-level, low-level and cultural musical features (i.e. symbolic, audio and web-mined features).

In order to meet these goals, all aspects of jMIR are open source and distributed free under a GNU General Public License. The software is well-documented and include GUIs in order to increase general usability. A special emphasis has been placed on software architectures that facilitate extensibility for those technically inclined users who wish to modify or add to the software.

More details are available in the academic publications on jMIR's components. The manuals and other documentation for each jMIR component also provide helpful documentation.

Each of the components comprising the jMIR software suite may be used entirely separately or as an integrated whole. The components communicate with each other using files in either ACE XML, Weka ARFF or CSV formats. The components are as follows:

Data Mining and Machine Learning

• ACE: Pattern recognition software that utilizes meta-learning. Evaluates, trains and uses a variety of classifiers, classifier ensembles and dimensionality reduction algorithms based on the needs of each particular research problem.
• ACE XML: Standardized file formats for representing information related to automatic music classification, including feature values, feature metadata, insance labels and class ontologies.
• jMIRUtilities: Tools for performing miscellaneous tasks, such as labelling instances, extracting data from Apple iTunes XML files, merging features extracted from different sources, etc.

Feature Extraction

• jSymbolic: Software for extracting high-level features from symbolic music encodings.
• jAudio: Software for extracting low and high-level features from audio recordings.
• jWebMiner: Software for extracting cultural features from the internet.
• jLyrics: Software for mining lyrics from the web and extracting textual features from them.

Education and Audio Production

• jProductionCritic: Educational software for automatically finding technical redording and production errors in audio files.

Data and Metadata

• jSongMiner: Software for identifying unknown audio and extracting metadata about songs, artists and albums from various web services and embedded sources.
• jMusicMetaManager: Software for profiling music collections and detecting metadata errors and redundancies.
• jMei2Midi: Library for converting MEI to MIDI. Can retain and communicate MEI information that cannot be represented in MIDI.
• Codaich, Bodhidharma MIDI and SLAC: Labeled datasets for training, testing and evaluating MIR systems.

Legacy

• Bodhidharma: MIREX 2005-winning software for classifying MIDI recordings by genre. The ancestor of ACE and jSymbolic.

Graphical Overview of Music Classification Tasks and Corresponding (Original) jMIR Components:

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