Clef: An Extensible, Experimental Framework for Music Information Retrieval
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CitationDeCurtins, Max. 2018. Clef: An Extensible, Experimental Framework for Music Information Retrieval. Master's thesis, Harvard Extension School.
AbstractText-based searching of textual data has long been a mainstay of computing, and
as search technology has evolved so has interest in searching non-textual data. In recent
years efforts to use image files as queries for other image files (or for information about
what is in the query image file) have profited from advances in machine learning, as have
other alternative search domains.
While searching for music using musical data has met with considerable success
in audio sampling software such as Shazam, searching machine-readable, music notation-
based data—also known as symbolic music data—using queries written in music
notation has lagged behind, with most development in this area geared toward academic
music researchers or existing in ad hoc implementations only. Music information retrieval—
the field concerned with developing search techniques for music—requires a framework
that can move beyond predetermined combinations of algorithms and datasets.
The Clef system demonstrates that this is possible using container-based services
that communicate with each other over HTTP. Clef offers an extensible approach to
building a musical search engine that allows new algorithms and datasets to be accessed
through a consistent, music notation-based user interface for query input. Extending the
system with a new container for running a music information retrieval algorithm requires
significant development, but once operational, new algorithm containers integrate seamlessly
into the user interface.
Citable link to this pagehttps://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37364558
- DCE Theses and Dissertations 
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