End-to-end solutions have brought about significant advances in the field of Optical Music Recognition. These approaches directly provide the symbolic representation of a given image of a musical score. Despite this, several documents, such as pianoform musical scores, cannot yet benefit from these solutions since their structural complexity does not allow their effective transcription. This paper presents a neural method whose objective is to transcribe these musical scores in an end-to-end fashion. We also introduce theGrandStaffdataset, which contains 53,882 single-system piano scores in common western modern notation. The sources are encoded in both a standard digital music representation and its adaptation for current transcription technologies. The method proposed in this paper is trained and evaluated using this dataset. The results show that the approach presented is, for the first time, able to effectively transcribe pianoform notation in an end-to-end manner.

We present an integrated vision-guided grasping system for service robots. Our system integrates computer vision to capture the shape of the objects, online grasp determination based on that shape, and image-based control for grasp execution. Novel techniques are presented to solve the problems concerned under the imposed resource constraints, namely, for information reduction and segmentation in image processing, strategies for grasp determination as well as vision-guided control for grasp execution. A preshaping heuristic strategy is combined with symmetry and local shape analysis. Also, the surface of contact between fingers and object is considered and a novel technique for sampling the visuomotor Jacobian is introduced. Experimental validation results are provided showing how the robot arm can efficiently and stably grasp unknown everyday objects.

Music genre meta-data is of paramount importance for the organisation of music repositories. People use genre in a natural way when entering a music store or looking into music collections. Automatic genre classification has become a popular topic in music information retrieval research both, with digital audio and symbolic data. This work focuses on the symbolic approach, bringing to music cognition some technologies, like the stochastic language models, already successfully applied to text categorisation. The representation chosen here is to model chord progressions as n-grams and strings and then apply perplexity and naiumlve Bayes classifiers, respectively, in order to assess how often those structures are found in the target genres. Some genres and sub-genres among popular, jazz, and academic music have been considered, trying to investigate how far can we reach using harmonic information with these models. The results at different leve! ls of the genre hierarchy for the techniques employed are presented and discussed.This work is supported by the Spanish CICyT PROSEMUS project (TIN2006-14932-C02), the research programme Consolider Ingenio 2010 (MIPRCV, CSD2007-00018) and the Pascal Network of Excellence