Parametric Estimations Based on Homomorphic Deconvolution for Time of Flight in Sound Source Localization System Vehiclemounted sound source localization systems provide comprehensive information to improve driving conditions by monitoring the surroundings. The threedimensional structure of vehicles hinders the omnidirectional sound localization system because of the long and uneven propagation. In the received signal, the flight times between microphones delivers the essential information to locate the sound source. This paper proposes a novel method to design a sound localization system based on the single analog microphone network. This article involves the flight time estimation for two microphones with nonparametric homomorphic deconvolution. The parametric methods are also suggested with Yulewalker, Prony, and SteiglitzMcBride algorithm to derive the coefficient values of the propagation model for flight time estimation. The nonparametric and SteiglitzMcBride method demonstrated significantly low bias and variance for 20 or higher ensemble average length. The Yulewalker and Prony algorithms showed gradually improved statistical performance for increased ensemble average length. Hence, the nonparametric and parametric homomorphic deconvolution well represent the flight time information. The derived nonparametric and parametric output with distinct length will serve as the featured information for a complete localization system based on machine learning or deep learning in future works.

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.