Learning-based methods have become ubiquitous in sound source localization (SSL). Existing systems rely on simulated training sets for the lack of sufficiently large, diverse and annotated real datasets. Most room acoustic simulators used for this purpose rely on the image source method (ISM) because of its computational efficiency. This paper argues that carefully extending the ISM to incorporate more realistic surface, source and microphone responses into training sets can significantly boost the real-world performance of SSL systems. It is shown that increasing the training-set realism of a state-of-the-art direction-of-arrival estimator yields consistent improvements across three different real test sets featuring human speakers in a variety of rooms and various microphone arrays. An ablation study further reveals that every added layer of realism contributes positively to these improvements.

翻译：现有系统因缺乏足够大、多样化和附加说明的真实数据集而依赖模拟培训组,大多数用于此目的的房间声学模拟器因其计算效率而依赖图像源法(ISM),本文认为,仔细扩大ISM,将更现实的表面、源和麦克风反应纳入培训组,可大大提升SSL系统的真实世界性表现。 事实证明,增加培训中最先进的抵达估计数字显示,在以不同房间和各种麦克风阵列的讲演人为特色的三个不同的实际测试组中,不断改进培训中的现实主义。