Graph Cepstrum: Spatial Feature Extracted from Partially Connected Microphones In this paper, we propose an effective and robust method of spatial feature extraction for acoustic scene analysis utilizing partially synchronized andor closely located distributed microphones. In the proposed method, a new cepstrum feature utilizing a graphbased basis transformation to extract spatial information from distributed microphones, while taking into account whether any pairs of microphones are synchronized andor closely located, is introduced. Specifically, in the proposed graphbased cepstrum, the logamplitude of a multichannel observation is converted to a feature vector utilizing the inverse graph Fourier transform, which is a method of basis transformation of a signal on a graph. Results of experiments using real environmental sounds show that the proposed graphbased cepstrum robustly extracts spatial information with consideration of the microphone connections. Moreover, the results indicate that the proposed method more robustly classifies acoustic scenes than conventional spatial features when the observed sounds have a large synchronization mismatch between partially synchronized microphone groups.

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.