CEGeME - Center for Research on Musical Gesture and Expression is affiliated to the Graduate Program in Music of the Universidade Federal de Minas Gerais (UFMG), hosted by the School of Music, Belo Horizonte, Brazil, since 2008. Focused on the empirical investigation of music performance, research at CEGeME departs from musical content information extracted from audio signals and three-dimensional spatial position of musicians, recorded during a music performance. Our laboratories are properly equipped for the acquisition of such data. Aiming at establishing a musicological approach to different aspects of musical expressiveness, we investigate causal relations between the expressive intention of musicians and the way they manipulate the acoustic material and how they move while playing a piece of music. The methodology seeks support on knowledge such as computational modeling, statistical analysis, and digital signal processing, which adds to traditional musicology skills. The group has attracted study postulants from different specialties, such as Computer Science, Engineering, Physics, Phonoaudiology and Music Therapy, as well as collaborations from professional musicians instigated by specific inquiries on the performance on their instruments. This paper presents a brief retrospective of the different research projects conducted at CEGeME.
Abstract:The field of physiology-based interaction and monitoring is developing at a fast pace. Emerging applications like fatigue monitoring often use sound to convey complex dynamics of biological signals and to provide an alternative, non-visual information channel. Most Physiology-to-Sound mappings in such auditory displays do not allow customization by the end-users. We designed a new sonification system that can be used for extracting, processing and displaying Electroencephalography data (EEG) with different sonification strategies. The system was validated with four user groups performing alpha/theta neurofeedback training (a/t) for relaxation that varied in feedback personalization (Personalized/Fixed) and a number of sonified EEG features (Single/Multiple). The groups with personalized feedback performed significantly better in their training than fixed mappings groups, as shown by both subjective ratings and physiological indices. Additionally, the higher number of sonified EEG features resulted in deeper relaxation than when training with single feature feedback. Our results demonstrate the importance of adaptation and personaliziation of EEG sonification according to particular applications, in our case, to a/t neurofeedback. Our experimental approach shows how user performance can be used for validating different sonification strategies.
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