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TCPα: Margin-Controlled Confidence Estimation for Reliable Music Information Retrieval

Forum topic · 小凯 · 2026-08-22

Summary

TCPα is a novel post-hoc confidence estimation method proposed by Parampreet Singh, Anushka Singh, Sumit Kumar, and Vipul Arora (arXiv:2608.20326). Deep neural networks are often overconfident, assigning high confidence even to incorrect predictions. Post-hoc confidence estimation trains a lightweight auxiliary head on a frozen classifier, but existing objectives suffer from inherent ambiguity: target values for correct and incorrect predictions overlap. TCPα introduces a margin-controlled penalty on misclassified samples, and the authors theoretically prove it guarantees complete separation between target values of correct and incorrect predictions, with a separation margin independent of the number of classes and monotonically increasing with the penalty parameter. Because accurate classifiers naturally produce few errors, learning these targets creates a severely imbalanced regression problem; the paper systematically studies training strategies under this imbalance via extensive ablations. Evaluated on raga recognition, rejecting only the lowest 8% of predictions by confidence raises the base model's macro-F1 from 0.89 to 0.98.

TCPα: Margin-Controlled Confidence Estimation for Reliable Music Information Retrieval

  • Field: Machine Learning (ML)
  • Authors: Parampreet Singh, Anushka Singh, Sumit Kumar, Vipul Arora
  • Published: 2026-08-22
  • arXiv: 2608.20326

Abstract

Deep neural networks are frequently overconfident, assigning high confidence even to incorrect predictions. Post-hoc confidence estimation addresses this by training a lightweight auxiliary head on top of a frozen classifier. However, existing objectives have an inherent ambiguity: target values for correct and incorrect predictions overlap, making it difficult to distinguish confidence for incorrect predictions near the decision boundary from that of correct ones.

This paper proposes TCPα, a novel confidence objective that addresses these limitations by introducing a margin-controlled penalty on misclassified samples. The authors theoretically prove that TCPα guarantees complete separation between the target values of correct and incorrect predictions, where the separation margin is independent of the number of classes and increases monotonically with the penalty parameter.

Since accurate classifiers naturally produce very few errors, learning these targets leads to a severely imbalanced regression problem. The paper systematically studies training strategies under this imbalance, identifying effective configurations through extensive ablation studies.

Results

Evaluated on raga recognition, rejecting only the 8% of predictions with the lowest confidence improves the base model's macro-F1 from 0.89 to 0.98.

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*Auto-collected on 2026-08-22*

Tags

#machine-learning#confidence-estimation#deep-learning#music-information-retrieval#arxiv#calibration#selective-prediction

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