Music Symbol Sequence Indexing in Medieval Plainchant Manuscripts

Abstract

Huge amounts of musical manuscripts are preserved in cathedrals, abbeys, and archives. However, without reliable transcripts, their contents are inaccessible. Manual transcription is unaffordable for large collections, and current automatic technologies—such as Optical Music Recognition or Handwritten Music Recognition—do not provide sufficient accuracy for a fully-automatic scenario. In many cases, perfect transcripts are not really needed, given that content-based search with some degree of reliability would already be extremely useful. Spotting just single music symbols is rather useless (most of the symbols generally appear in all pages); instead, helpful search targets are melodic patterns, which typically correspond to music symbol sequences. We explore approaches for accurate retrieval of melodic patterns, represented by music symbol sequences, from collections of Medieval plainchant manuscripts. Our statistical framework, based on the use of convolutional recurrent neural networks and probabilistic indices, is shown to be useful for retrieving music patterns which appear frequently in this untranscribed images, yielding an Average Precision of 86 %.