Short and Long Term Representation of an Unfamiliar Tone Distribution

We report on a study conducted to extend our knowledge about the process of gaining a mental representation of music. Several studies, inspired by research on the statistical learning of language, have investigated statistical learning of sequential rules underlying tone sequences. Given that the mental representation of music correlates with distributional properties of music, we tested whether participants are able to abstract distributional information contained in tone sequences to form a mental representation. For this purpose, we created an unfamiliar music genre defined by an underlying tone distribution, to which 40 participants were exposed. Our stimuli allowed us to differentiate between sensitivity to the distributional properties contained in test stimuli and long term representation of the distributional properties of the music genre overall. Using a probe tone paradigm and a two-alternative forced choice discrimination task, we show that listeners are able to abstract distributional properties of music through mere exposure into a long term representation of music. This lends support to the idea that statistical learning is involved in the process of gaining musical knowledge.

Photonic Guitar Pick-up: Fiber Strain Sensors Find Applications in Music Recording

In this report we give a summary of our work on the development of low-noise fiber-optic strain sensors. Three types of strain sensors were developed and were tested by attaching them to the bodies of acoustic guitars. The fibers are strained as the soundboards of the guitars vibrate. The resulting spectral shift of either a Fiber Bragg Grating or a fiber Fabry-Perot cavity is then used to record the sound of the instrument.