SLoClas: A Database for Joint Sound Localization and Classification

Amine El Abridi; Bidisha Sharma; Haizhou Li; Xinyuan Qian

SLoClas: A Database for Joint Sound Localization and Classification

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arxiv 2108.02539 v1 pith:5IXNWWM2 submitted 2021-08-05 cs.SD cs.DBeess.AS

Xinyuan Qian , Bidisha Sharma , Amine El Abridi , Haizhou Li This is my paper · ORCID

classification cs.SDcs.DBeess.AS

keywords classificationlocalizationsounddatabasedegreesloclasarraycorpus

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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.

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