In this article, we describe Conditioned Localizer and Classifier (CoLoC) which is a novel solution for Sound Event Localization and Detection (SELD). The solution constitutes of two stages: the localization is done first and is followed by classification conditioned by the output of the localizer. In order to resolve the problem of the unknown number of sources we incorporate the idea borrowed from Sequential Set Generation (SSG). Models from both stages are SELDnet-like CRNNs, but with single outputs. Conducted reasoning shows that such two single-output models are fit for SELD task. We show that our solution improves on the baseline system in most metrics on the STARSS22 Dataset.

翻译：在本文中,我们描述了有条件的本地化和分类(CoLOC),这是合理事件本地化和检测(SELD)的新解决方案。解决方案分为两个阶段:首先是本地化,然后是按本地化输出进行分类。为了解决未知数量的来源问题,我们采用了从序列组代(SSG)借用的理念。两个阶段的模型是类似于SELDnet的CRNNs,但有单一输出。进行推理表明,这两种单产出模型适合SELD的任务。我们表明,我们的解决办法在StarSS22数据集的大多数衡量标准中都改善了基线系统。