$TCP_α$: Margin-Controlled Confidence estimation for reliable Music Information Retrieval

Music information retrieval has seen rapid progress with deep learning, notably CNNs and LSTMs for tasks like genre classification, instrument recognition, and rāga identification. Despite high accuracy, models tend to be overconfident, which hampers their deployment in critical applications such as automated transcription, recommendation, and educational tools. Existing confidence measures like MCP, TCP, and calibration methods address uncertainty quantification but struggle to reliably distinguish failures, especially in high-accuracy regimes with many classes. This gap limits the practical utility of confidence scores for error detection, rejection, and domain adaptation, motivating the development of more robust, theoretically grounded confidence targets.

The main challenge is that current confidence targets, such as TCP and TCPn, cannot guarantee a clear separation between correct and incorrect predictions, especially as the number of classes grows. This overlap leads to unreliable failure detection, undermining applications requiring high trustworthiness. Additionally, the highly imbalanced nature of training data—where errors are rare—makes learning effective confidence predictors difficult. Addressing these issues requires a new target formulation with provable separation guarantees and strategies to handle extreme class imbalance during training.

This work introduces TCPα, a novel confidence target that incorporates a margin parameter α to control the separation boundary. Unlike TCP and TCPn, TCPα guarantees a class-independent, complete separation between success and error predictions, with the margin increasing monotonically with α. Theoretical proofs establish that the success targets are always 1, while error targets are bounded below 1/2(1+α). The training strategy employs stratified sampling and class-weighting to effectively learn from the highly skewed data, ensuring that rare errors are accurately modeled. This combination of theoretical guarantees and practical training techniques marks a significant advancement over existing methods.

• �� Design TCPα by adding a margin penalty to the TCPn denominator, controlled by α. • Prove that success targets are always 1, and error targets are bounded below 1/2(1+α), ensuring complete separation. • Implement stratified mini-batch sampling to maintain a fixed error-to-success ratio, preventing bias towards abundant success samples. • Apply class-conditional weighting to emphasize rare error samples during training. • Use a two-stage process: pre-train the classifier, freeze parameters, then train the confidence head with regression loss against TCPα targets. • Validate the approach across multiple datasets and tasks, including domain shift scenarios, to demonstrate robustness and generalization.

The experiments involve rāga identification on the PIM dataset and ornamentation detection on a frame-wise annotated corpus. Baselines include MCP, TCP, TCPn, and other uncertainty metrics like predictive entropy and energy score. Evaluation metrics cover FPR@95%, AUROC, and AUPR. The training involves tuning α, employing stratified sampling, and class-weighting to address imbalance. Ablation studies compare different training strategies, confirming the effectiveness of the proposed approach. Domain shift tests with minimal labeled data further demonstrate robustness. Results consistently show TCPα's superiority in failure prediction, enabling effective rejection of low-confidence predictions and significant performance gains.

Rejecting only 8% of the lowest confidence predictions with TCPα improves macro-F1 from 0.89 to 0.98 in rāga identification, outperforming TCP and TCPn. Domain adaptation experiments show that fine-tuning with 5% labeled samples restores performance close to the original. Ornamentation detection experiments, without changing configurations, achieve similar improvements, confirming the method’s robustness. Theoretical analysis guarantees class separation and margin growth with α, providing a reliable foundation for failure prediction. These results demonstrate TCPα’s ability to significantly enhance the reliability of confidence estimates in high-accuracy, multi-class settings.

This framework is directly applicable to automatic music tagging, failure detection in music transcription, and domain adaptation tasks. It enables systems to reject uncertain predictions, reducing errors and increasing user trust. The method can also be integrated into recommendation engines and educational tools, where reliable confidence estimates are critical. Its ability to handle high accuracy and class imbalance makes it suitable for deployment in real-world, large-scale music archives and streaming platforms, improving overall system robustness and interpretability.

While TCPα guarantees class separation, its effectiveness diminishes in scenarios with extremely low error rates or highly noisy classifiers. The approach depends on the quality of the base classifier; poor initial models limit confidence prediction accuracy. Parameter tuning for α remains necessary, and computational costs increase with ensemble or complex training strategies. Future work should explore adaptive parameter tuning, unsupervised learning, and multi-task extensions to further enhance robustness and applicability.