Adding self-supervised objectives to a supervised multi-pitch estimator improves closed-set performance but triggers degeneration to blank predictions on additional, unlabeled data.
Our method- ology can be viewed as the integration of self-supervised techniques for MPE [10] into a supervised framework
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.AS 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Investigating an Overfitting and Degeneration Phenomenon in Self-Supervised Multi-Pitch Estimation
Adding self-supervised objectives to a supervised multi-pitch estimator improves closed-set performance but triggers degeneration to blank predictions on additional, unlabeled data.