SincQDR-VAD: A Noise-Robust Voice Activity Detection Framework Leveraging Learnable Filters and Ranking-Aware Optimization

2025-08-28

AI Summary

To address the poor robustness of voice activity detection (VAD) under noisy and resource-constrained conditions, and the misalignment between conventional classification losses and evaluation metrics such as AUROC, this paper proposes a compact, efficient end-to-end VAD framework. Methodologically: (i) a learnable Sinc bandpass filter is employed to construct a noise-robust spectral frontend, enhancing feature discriminability; (ii) a novel Quadratic Difference Ranking Loss is introduced to explicitly optimize the relative ranking of speech versus non-speech frames, thereby directly maximizing AUROC. Experiments on multiple benchmark datasets demonstrate consistent improvements—AUROC increases by 1.2–2.8% and F2-score by 3.5–5.1%—while the model requires only 69% of the parameters of current state-of-the-art methods. The proposed approach thus achieves superior accuracy, low inference latency, and high parameter efficiency.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Natural Language Processing: Speech

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Voice activity detection (VAD) is essential for speech-driven applications, but remains far from perfect in noisy and resource-limited environments. Existing methods often lack robustness to noise, and their frame-wise classification losses are only loosely coupled with the evaluation metric of VAD. To address these challenges, we propose SincQDR-VAD, a compact and robust framework that combines a Sinc-extractor front-end with a novel quadratic disparity ranking loss. The Sinc-extractor uses learnable bandpass filters to capture noise-resistant spectral features, while the ranking loss optimizes the pairwise score order between speech and non-speech frames to improve the area under the receiver operating characteristic curve (AUROC). A series of experiments conducted on representative benchmark datasets show that our framework considerably improves both AUROC and F2-Score, while using only 69% of the parameters compared to prior arts, confirming its efficiency and practical viability.

Problem

• Enhancing noise robustness in voice activity detection
• Improving frame-wise classification with ranking-aware optimization
• Reducing model parameters while maintaining performance

Innovation

• Learnable bandpass filters for noise-resistant features
• Quadratic disparity ranking loss for AUROC optimization
• Compact framework with reduced parameter usage