In this work, we present the development of a new database, namely Sound Localization and Classification (SLoClas) corpus, for studying and analyzing sound localization and classification. The corpus contains a total of 23.27 hours of data recorded using a 4-channel microphone array. 10 classes of sounds are played over a loudspeaker at 1.5 meters distance from the array by varying the Direction-of-Arrival (DoA) from 1 degree to 360 degree at an interval of 5 degree. To facilitate the study of noise robustness, 6 types of outdoor noise are recorded at 4 DoAs, using the same devices. Moreover, we propose a baseline method, namely Sound Localization and Classification Network (SLCnet) and present the experimental results and analysis conducted on the collected SLoClas database. We achieve the accuracy of 95.21% and 80.01% for sound localization and classification, respectively. We publicly release this database and the source code for research purpose.

中文翻译：

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SLoClas：联合声音定位和分类数据库

在这项工作中，我们提出了一个新数据库的开发，即声音定位和分类 (SLoClas) 语料库，用于研究和分析声音定位和分类。该语料库包含使用 4 通道麦克风阵列记录的总共 23.27 小时的数据。通过以 5 度的间隔将到达方向 (DoA) 从 1 度变为 360 度，在距阵列 1.5 米的扬声器上播放 10 类声音。为了便于噪声鲁棒性的研究，使用相同的设备在 4 个 DoA 记录了 6 种类型的室外噪声。此外，我们提出了一种基线方法，即声音定位和分类网络（SLCnet），并展示了对收集的 SLoClas 数据库进行的实验结果和分析。我们达到了 95.21% 和 80 的准确率。01% 分别用于声音定位和分类。我们公开发布此数据库和源代码用于研究目的。