Violin performance analysis requires accurate and robust f0 estimates to give feedback on the playing accuracy. Despite the recent advancements in data-driven f0 estimators, their application to performance analysis remains a challenge due to style-specific and dataset-induced biases. In this paper, we address this problem by introducing Violin Etudes, a 27.8-hours violin performance dataset constructed with domain knowledge in instrument pedagogy and a novel automatic f0-labeling paradigm. Experimental results on unseen datasets show that the CREPE f0 estimator trained on Violin Etudes outperforms the widely-used pre-trained version trained on multiple manually-labeled datasets. Further preliminary findings suggest that (i) existing data-driven f0 estimators may overfit to equal temperament, and (ii) iterative re-labeling regularized by our novel Constrained Harmonic Resynthesis method can simultaneously enhance datasets and f0 estimators. Our dataset curation methodology is easily scalable to other instruments owing to the quantity of pedagogical data online. It also supports a range of MIR research directions thanks to the performance difficulty labels from educational institutions.

We would like to thank Esteban Maestre for his valuable insights on the violin resonance structure. This research was carried out under the project Musical AI - PID2019- 111403GB-I00/AEI/10.13039/501100011033, funded by the Spanish Ministerio de Ciencia e Innovación and the Agencia Estatal de Investigación.

Comunicació presentada a 23nd International Society for Music Information Retrieval Conference (ISMIR 2022), celebrat del 4 al 8 de desembre de 2022 a Bangalore, Índia.