A musical data set for note-level segmentation of monophonic music is presented. It contains 36 excerpts from commercial recordings of monophonic classical western music and features the instrument groups strings, woodwind and brass. The excerpts are self-contained phrases with a mean length of 17.97 seconds and an average of 20 notes. All phrases are played in moderate tempo, mostly with significant amounts of expressive articulation. A manually annotated ground truth splits each item into a sequence of the three states note, transition and rest. The set is designed as an open source project, aiming at the development and evaluation of algorithms for segmentation, music performance analysis and feature selection. This paper presents the process of ground truth labeling and a detailed description of the data set and its properties.

Authors: von Coler, Henrik; Lerch, Alexander
Affiliations: Georgia Tech Center for Music Technology, Atlanta, GA, USA; SIM - Staatliches Institut für Musikforschung Preußischer Kulturbesitz, Berlin, Germany(See document for exact affiliation information.)
AES Conference: 53rd International Conference: Semantic Audio (January 2014)
Paper Number: P2-3
Publication Date: January 27, 2014
Subject: Automatic Music Transcription

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