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 %.

There are large collections of music manuscripts preserved over the centuries. In order to analyze these documents it is necessary to transcribe them into a machine-readable format. This process can be done automatically using Optical Music Recognition (OMR) systems, which typically consider segmentation plus classification workflows. This work is focused on the latter stage, presenting a comprehensive study for classification of handwritten musical symbols using Convolutional Neural Networks (CNN). The power of these models lies in their ability to transform the input into a meaningful representation for the task at hand, and that is why we study the use of these models to extract features (Neural Codes) for other classifiers. For the evaluation we consider four datasets containing different configurations and notation styles, along with a number of network models, different image preprocessing techniques and several supervised learning classifiers. Our results show that a remarkable accuracy can be achieved using the proposed framework, which significantly outperforms the state of the art in all datasets considered.

Music is a key element for cultural transmission, and so large collections of music manuscripts have been preserved over the centuries. In order to develop computational tools for analysis, indexing and retrieval from these sources, it is necessary to transcribe the content to some machine-readable format. In this paper we discuss the Handwritten Music Recognition problem, which refers to the development of automatic transcription systems for musical manuscripts. We focus on mensural notation, one of the most widespread varieties of Western classical music. For that, we present a labeled corpus containing 576 staves, along with a baseline recognition system based on a combination of hidden Markov models and N-gram language models. The baseline error obtained at symbol level is about 40 % which, given the difficulty of the task, can be considered a good starting point for future developments. Our aim is that these data and preliminary results help to promote this research field, serving as a reference in future developments.