Development of a Location estimation System for Minute Sound Source by Using Human Acoustic System with Stochastic Resonance The purpose of this study is to develop a system that enables location estimation of a small sound source. The location estimation of a small sound source has some difficulties such as high computational costs or disturbances from the ambient noises and reflected waves. The proposed system is composed of a biologicallyinspired system which uses a hearing mechanism based on the human auditory model and a mechanism for perceiving weak sound signals that use stochastic resonance. The location estimation mechanism in the proposed system is based on the timelag detecting architecture. On the other hand, the stochastic resonance mechanism can pick up the small sound signals among the ambient noises. Using this proposed system, we implemented the location estimation of the small sound source through hardware experiments. Good results were obtained for the small sound source location estimation.

Localization and classification of overlapping sound events based on spectrogramkeypoint using acousticsensornetwork data Automatic localization and classification of environmental sound events can provide great aid to many humancentric IoT 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. Unlike most works, which decompose and classify overlapping signals using a unified model, we first decompose overlapping sound signals with a spectrogramkeypoint based localization algorithm. These located and clustered spectrogramkeypoints are subsequently reused for sound source classification. Our major contribution is the modeling of a global cost function to synchronize the timedifferenceofarrivals (TDOA) of each small spectrogramkeypoint and further locating the sound sources by these clustered keypoints. With these clustered keypoints, 2 different classification models are used to classify the sound sources. Our experiments show that our solution is both accurate and lowcost in terms of calculation effort.