The SEILS Dataset: Symbolically Encoded Scores in Modern-Early Notation for Computational Musicology

The automatic analysis of notated Renaissance music is restricted by a shortfall in codified repertoire. Thousands of scores have been digitised by music libraries across the world, but the absence of symbolically codified information makes these inaccessible for computational evaluation. Optical Music Recognition (OMR) made great progress in addressing this issue, however, early notation is still an on-going challenge for OMR. To this end, we present the Symbolically Encoded Il Lauro Secco (SEILS) dataset, a new dataset of codified scores for use within computational musicology. We focus on a collection of Italian madrigals from the 16 century, a polyphonic secular a cappella composition characterised by strong musical-linguistic synergies. Thirty madrigals for five unaccompanied voices are presented in modern and early notation, considering a variety of digital formats: Lilypond, Music XML, MIDI, and Finale (a total of 150 symbolically codified scores). Given the musical and poetic value of the chosen repertoire, we aim to promote synergies between computational musicology and linguistics.