Summary
This paper introduces TCP_alpha, a margin-controlled confidence estimation method for music information retrieval (MIR). Deep neural networks are often overconfident, assigning high confidence even to incorrect predictions, leaving users without a reliable signal for deciding when to trust outputs. Post-hoc confidence estimation addresses this by training a lightweight auxiliary head on a frozen classifier, but existing objectives are inherently ambiguous: they assign overlapping confidence values to correct and incorrect predictions, while errors near the decision boundary are underconfident. TCP_alpha uses a margin-based objective that assigns distinct confidence values according to a prediction's distance from the decision boundary, explicitly penalizing both overconfident and underconfident predictions. Experiments across multiple MIR tasks show that TCP_alpha outperforms existing methods in confidence calibration and error detection, providing more trustworthy confidence estimates for reliable MIR systems. Paper: arXiv 2608.20326 by Parampreet Singh, Anushka Singh, Sumit Kumar, and Vipul Arora.
Paper Overview
Field: Machine Learning
Authors: Parampreet Singh, Anushka Singh, Sumit Kumar, Vipul Arora
Published: 2026-08-22
arXiv: 2608.20326
Abstract
Deep neural networks are often overconfident, assigning high confidence even to incorrect predictions. Consequently, users lack a reliable signal for deciding when a prediction can be trusted.
Key Contributions
- Post-hoc confidence estimation methods train a lightweight auxiliary head on a frozen classifier, but existing objectives are inherently ambiguous: they assign overlapping confidence values to correct and incorrect predictions, while errors near the decision boundary remain underconfident.
- The paper proposes TCP_alpha, a margin-controlled confidence estimation method for music information retrieval (MIR).
- TCP_alpha employs a margin-based objective that assigns distinct confidence values according to the prediction's distance from the decision boundary, explicitly penalizing both overconfident and underconfident predictions.
- Experiments across multiple MIR tasks show that TCP_alpha outperforms existing methods in both confidence calibration and error detection, offering more trustworthy confidence estimates for reliable MIR systems.
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