A study on attention-based fine-grained image recognition: Towards musical instrument performing hand shape assessment

Abstract

Automatic identification and professional evaluation makes musical instrument learning more intelligent. Since a proper hand shape is the basis of fingerings in playing instruments, this paper explores an integration of intelligent recognition technique into hand shape assessment of instrument players in an attempt of taking Chinese zither (Zheng) as an example. The fine-grained image recognition is novelly applied to automatically assessing basic hand shapes, as a tentative exploration of interdisciplinary research. First, this paper formulates an assessment scales by combining fine-grained image features with hand shape evaluation indicators in musical instrument learning. Then, an image dataset for hand shapes of Chinese zither performance (CZ-Dataset V2) is established based on free multi-view acquisition. Finally, we propose a fine-grained hand shape image recognition method using attention mechanism. Experimental results show that the basic instrumental hand shapes can be effectively recognized and reasonable suggestions for hand shape assessment can be provided.