MFAE: Masked frame-level autoencoder with hybrid-supervision for low-resource music transcription

Abstract

Automantic Music Transcription (AMT) is an essential topic in music information retrieval (MIR), and it aims to transcribe audio recordings into symbolic representations. Recently, large-scale piano datasets with high-quality notations have been proposed for high-resolution piano transcription, which resulted in domain-specific AMT models achieved state-of- the-art results. However, those methods are hardly generalized to other ’low-resource’ instruments (such as guitar, cello, clarinet, etc.) transcription. In this paper, we propose a hybrid-supervised framework, the masked frame-level autoencoder (MFAE), to solve this issue. The proposed MFAE reconstructs the frame-level features of low-resource data to understand generic representations of low-resource instruments and improves low-resource transcription performance. Experimental results on several low- resource datasets (MAPS, MusicNet, and Guitarset) show that our framework achieves state-of-the-art performance in note-wise scores (Note F1 83.4%\64.1%\86.7%, Note-with-offset F1 59.8%\41.4%\71.6%). Moreover, our framework can be well generalized to various genres of instrument transcription, both in data-plentiful and data-limited scenarios.