VOCANO: A note transcription framework for singing voice in polyphonic music
Jui-Yang Hsu, Su Li · Zenodo (CERN European Organization for Nuclear Research) · 2021
High variability of singing voice and insufficiency of note event annotation present a huge bottleneck in singing voice transcription (SVT). In this paper, we present VOCANO, an open-source VOCAl NOte transcription framework built upon robust neural networks with multi-task and semi-supervised learning. Based on a state-of-the-art SVT method, we further consider virtual adversarial training (VAT), a semi-supervised learning (SSL) method for SVT on both clean and accompanied singing voice data, the latter being pre-processed using the singing voice separation (SVS) technique. The proposed framework outperforms the state of the arts on public benchmarks over a wide variety of evaluation metrics. The effects of the types of training models and the sizes of the unlabeled datasets on the performance of SVT are also discussed.