Enhancing Violin Fingering Generation through Audio-Symbolic Fusion

Abstract

The selection of violin fingerings is influenced by factors such as musical context, skill level, and personal taste. Current deep-learningbased models, relying solely on symbolic data, are able to generate playable fingerings but struggle to capture the personal nuances of musical performance, which only lie in the audio data. To address this limitation, we introduce a novel model that incorporates both audio and symbolic data, allowing users to upload music scores and their corresponding violinist recordings to obtain personalized fingerings related to the audio data. To simulate such a real-world application scenario, we also collect a new dataset from online audios. The experiment results demonstrate the superiority of our proposed method over previous symbolic-based methods, even in the situations involving multiple instruments in audio.