This paper presents an early version of an open extendable research and educational platform to support users in learning and mastering the different types of rare-singing. The platform is interfaced with a portable helmet to synchronously capture multiple signals during singing in a non-laboratory environment. Collected signals reflect articulatory movements and induced vibrations. The platform consists of four main modules: i) a capture and recording module, ii) a data replay (post processing) module, iii) an acoustic auto adaptation learning module, iv) and a 3D visualization sensory motor learning module. Our demo will focus on the first two modules. The system has been tested on two rare endangered singing musical styles, the Corsican Cantu in Paghjella, and the Byzantine hymns from Mount Athos, Greece. The versatility of the approach is further demonstrated by capturing a contemporary singing style known as Human Beat Box.
Bibliographic reference. Chawah, P. / Kork, S. K. Al / Fux, T. / Adda-Decker, Martine / Amelot, A. / Audibert, N. / Denby, B. / Dreyfus, G. / Jaumard-Hakoun, A. / Pillot-Loiseau, C. / Roussel, P. / Stone, M. / Xu, Kele / Crevier-Buchman, L. (2014): "An educational platform to capture, visualize and analyze rare singing", In INTERSPEECH-2014, 2128-2129.