CNNBased Accidental Detection in Dense Printed Piano Scores The recognition of mid18th to mid20th century piano scores presents segmentation challenges caused by touching and broken symbols produced by imprinting techniques and time degradation. We present a new notehead accidental dataset containing 2955 images from dense and damaged piano scores. We address this detection problem with very small training samples using a simple Spatial Transformer (ST)based Convolutional Neural Network detector improved through bootstrapping and contextual information, and more powerful deep learning detectors (Faster RCNN, RFCN, and SSD) with transferlearning on the COCO dataset. We trained all our detectors using 5 fold crossvalidation and obtain 98.73% mean Average Precision (mAP) for an Intersection over Union (IoU) threshold of 0.75 with our best detector. Our STbased detector obtains a slightly lower mAP of 94.81%, but runs 40 times faster, and uses 18 times less memory.

Music Symbol Sequence Indexing in Medieval Plainchant Manuscripts 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 technologiesxe2x80x94such as Optical Music Recognition or Handwritten Music Recognitionxe2x80x94do not provide sufficient accuracy for a fullyautomatic scenario. In many cases, perfect transcripts are not really needed, given that contentbased 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 %.