TrumpetNet: A Convolutional Neural Network with Self-Attention Mechanisms for visual detection of trumpet fingering

Abstract

Visual detection of fingering on the trumpet is an increasingly interesting topic in music research. The ability to recognize and track the movements of the trumpet player’s fingers during the performance of a musical piece can provide valuable information for analyzing and improving instrument technique. However, this is a largely unexplored task, as most works focus on audio quality rather than instrument fingering techniques. Developing techniques for identifying essential finger positions on a musical instrument is crucial, as poor fingering techniques can harm instrument performance. In this work, we propose the visual detection of this fingering using convolutional neural networks with a proprietary dataset created for this purpose. Additionally, to improve the results and focus on the essential parts of the instrument, we use self-attention mechanisms by extracting these features automatically.