k-SpecNET: Localization and classification of indoor superimposed sound for acoustic sensor networks
Wei Wang, Fatjon Seraj, Paul Havinga · 2020
Automatic localization and classification of environmental sound events can provide great aid to many human-centric applications. However as many papers have mentioned, environmental sound events in daily life are complicated and hard to classify especially when multiple sounds happen simultaneously. Being different from many other works, we use an acoustic-sensor-network to solve this problem and decompose overlapping sound events using a sound localization model. The core of our contribution is to first find and locate the keypoints from each microphone's spectrogram and then aggregate them. With these aggregated keypoints as input, we then use 2 different classification models to further classify the type of sound sources. Compared with other classification models that only use single microphone, our experiments show that our solution is both accurate and low-cost in terms of calculation effort.