标题： SincQDR-VAD：一种利用可学习滤波器和排名感知优化的噪声鲁棒语音活动检测框架 显示英文标题

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

摘要： 语音活动检测（VAD）对于语音驱动的应用至关重要，但在噪声和资源受限的环境中仍远未完善。 现有方法在噪声方面缺乏鲁棒性，且它们的帧级分类损失与VAD的评估指标仅松散耦合。 为解决这些挑战，我们提出了SincQDR-VAD，这是一种紧凑且鲁棒的框架，结合了Sinc提取器前端和一种新颖的二次差异排序损失。 Sinc提取器使用可学习的带通滤波器来捕捉抗噪声的频谱特征，而排序损失则优化语音和非语音帧之间的成对得分顺序，以提高接收者操作特性曲线下的面积（AUROC）。 在代表性基准数据集上进行的一系列实验表明，我们的框架显著提高了AUROC和F2-Score，同时仅使用了先前方法69%的参数，证实了其效率和实际可行性。 显示英文摘要

摘要： 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.