Clef: An Extensible, Experimental Framework for Music Information Retrieval

Abstract

Text-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.