This paper introduces an ensemble of discriminators that improves the accuracy of a domain adaptation technique for the localization of multiple sound sources. Recently, deep neural networks have led to promising results for this task, yet they require a large amount of labeled data for training. Recording and labeling such datasets is very costly, especially because data needs to be diverse enough to cover different acoustic conditions. In this paper, we leverage acoustic simulators to inexpensively generate labeled training samples. However, models trained on synthetic data tend to perform poorly with real-world recordings due to the domain mismatch. For this, we explore two domain adaptation methods using adversarial learning for sound source localization which use labeled synthetic data and unlabeled real data. We propose a novel ensemble approach that combines discriminators applied at different feature levels of the localization model. Experiments show that our ensemble discrimination method significantly improves the localization performance without requiring any label from the real data.

翻译：本文引入了一组导师, 这些导师提高了域适应技术的准确性, 使多声源本地化。 最近, 深神经网络为这项任务带来了有希望的成果, 但是它们需要大量的标签数据来进行培训。 记录和标签这类数据集非常昂贵, 特别是因为数据需要多样化, 以涵盖不同的声学条件。 在本文中, 我们利用声学模拟器来廉价生成标签培训样本。 然而, 合成数据培训模型往往与真实世界记录不匹配而表现不佳。 为此, 我们探索两种对称性适应方法, 使用对称性合成数据和未标签真实数据的可靠源本地化的对称性学习。 我们建议一种新型的共性方法, 将适用于本地化模型不同特性层面的歧视问题结合起来。 实验显示, 我们的共性歧视方法在不需要真实数据标签的情况下, 大大改善了本地化绩效 。