SLoClas: A Database for Joint Sound Localization and Classification

Abstract

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.

arXiv commentSubmitted to O-COCOSDA 2021

References in corpus (9)

• Adam: A Method for Stochastic Optimization
• Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks
• Multi-Speaker DOA Estimation Using Deep Convolutional Networks Trained with Noise Signals
• Deep Neural Networks for Multiple Speaker Detection and Localization
• Is Someone Speaking? Exploring Long-term Temporal Features for Audio-visual Active Speaker Detection
• Polyphonic Sound Event Detection and Localization using a Two-Stage Strategy
• A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection
• Multi-Tones' Phase Coding (MTPC) of Interaural Time Difference by Spiking Neural Network
• Multi-target DoA Estimation with an Audio-visual Fusion Mechanism