Applying Deep Learning Techniques to Estimate Patterns of Musical Gesture.

ABSTRACT: Repetitive practice is one of the most important factors in improving the performance of motor skills. This paper focuses on the analysis and classification of forearm gestures in the context of violin playing. We recorded five experts and three students performing eight traditional classical violin bow-strokes: martelé, staccato, detaché, ricochet, legato, trémolo, collé, and col legno. To record inertial motion information, we utilized the Myo sensor, which reports a multidimensional time-series signal. We synchronized inertial motion recordings with audio data to extract the spatiotemporal dynamics of each gesture. Applying state-of-the-art deep neural networks, we implemented and compared different architectures where convolutional neural networks (CNN) models demo

SUBMITTER: Dalmazzo D 

PROVIDER: S-EPMC7813937 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature