提出新数据集与评估方法，解决说话人间歇移动时的跟踪身份混淆问题。
Tracking of Intermittent and Moving Speakers : Dataset and Metrics
- 构建首个支持说话人静音期位置变化的声学场景数据集LibriJump。
- 发现传统跟踪指标在断续轨迹上表现不佳，需结合关联度量提升评估精度。
- 适用于语音跟踪、多源分离及智能音频系统研发人员。
本文研究间歇性移动声源的跟踪问题，即声源在静音期间可能改变位置，此类情况极少被探索。现有方法多依赖空间观测进行轨迹身份管理，或通过预测有序位置实现联合定位与跟踪，但其在非连续轨迹（如静音期间转向）下的身份保持能力存疑。为此，本文提出首个基于一阶全向体声学格式的声学场景数据集LibriJump，包含静音期位置变化的说话人，模拟断续轨迹。为评估身份分配性能，引入计算机视觉领域适配的跟踪关联度量。实验表明，关联度量与传统跟踪指标在连续与断续轨迹下具有互补性。
原文摘要 · Abstract (English)
This paper presents the problem of tracking intermittent and moving sources, i.e, sources that may change position when they are inactive. This issue is seldom explored, and most current tracking methods rely on spatial observations for track identity management. They are either based on a previous localization step, or designed to perform joint localization and tracking by predicting ordered position estimates. This raises concerns about whether such methods can maintain reliable track identity assignment performance when dealing with discontinuous spatial tracks, which may be caused by a change of direction during silence. We introduce LibriJump, a novel dataset of acoustic scenes in the First Order Ambisonics format focusing on speaker tracking. The dataset contains speakers with changing positions during inactivity periods, thus simulating discontinuous tracks. To measure the identity assignment performance, we propose to use tracking association metrics adapted from the computer vision community. We provide experiments showing the complementarity of association metrics with previously used tracking metrics, given continuous and discontinuous spatial tracks.
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