A quantitative assessment of students’ experiences of studying music: a Spanish perspective on the European credit transfer system (ECTS)

The purpose of this investigation is to evaluate whether or not the allocation of time proposed in the Music Study Guide, adapted from the European Higher Education Area (EHEA) guidelines, is consistent and adequate for students with minimal musical knowledge. The report takes into account the importance of students’ previous knowledge and the relation this has to the time and effort expended by students in acquiring appropriate knowledge and skills. This is related also to the adequacy of the course specification to meet the demands of university study and the labour market. Results show that those students who enrolled at university without any previous musical knowledge are likely to experience significant difficulty in the acquisition of certain musical and professional competences. This highlights a need to reinforce the music curriculum, or establish zero-level courses, in order to enable such students to succeed in the subject.

Human-computer interaction for optical music recognition tasks

The need to digitise music scores has led to the development of Optical Music Recognition (OMR) tools. Unfortunately, the performance of these systems is still far from providing acceptable results. This situation forces the user to be involved in the process due to the need of correcting the mistakes made during recognition. However, this correction is performed over the output of the system, so these interventions are not exploited to improve the performance of the recognition. This work sets the scenario in which human and machine interact to accurately complete the OMR task with the least possible effort for the user.

Recognition of online handwritten music symbols

Paper submitted to MML 2013, 6th International Workshop on Machine Learning and Music, Prague, September 23, 2013.

GPGPU implementation of growing neural gas: application to 3D scene reconstruction

Self-organising neural models have the ability to provide a good representation of the input space. In particular the Growing Neural Gas (GNG) is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation. However, this type of learning is time-consuming, especially for high-dimensional input data. Since real applications often work under time constraints, it is necessary to adapt the learning process in order to complete it in a predefined time. This paper proposes a Graphics Processing Unit (GPU) parallel implementation of the GNG with Compute Unified Device Architecture (CUDA). In contrast to existing algorithms, the proposed GPU implementation allows the acceleration of the learning process keeping a good quality of representation. Comparative experiments using iterative, parallel and hybrid implementations are carried out to demonstrate the effectiveness of CUDA implementation. The results show that GNG learning with the proposed implementation achieves a speed-up of 6× compared with the single-threaded CPU implementation. GPU implementation has also been applied to a real application with time constraints: acceleration of 3D scene reconstruction for egomotion, in order to validate the proposal.

Music and movement: A comparative study between the BAPNE and Suzuki Methods

The aim of this article is to compare the Suzuki and BAPNE methods based on bibliography published for both approaches. In the field of musical and instrumental education and especially for the childhood stage, the correct use of the body and voice are of fundamental importance. These two methods differ from one another; one principally musical and instrumental, which is the Suzuki method, and one non-musical, the BAPNE method, which aims at stimulating attention, concentration, memory and the executing function of the pupil through music and body percussion. Comparing different approaches may provide teachers with a useful insight for facing different issues related to their discipline.