  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.
