Audio Event Detection Using Wireless Sensor Networks Based on Deep Learning

Jose Marie Mendoza, Vanessa Tan, Vivencio Fuentes Jr., Gabriel Perez, Nestor Michael C. Tiglao

Abstract: Wireless acoustic sensor network is useful for ambient assisted living applications. Its capability of incorporating an audio event detection and classification system helps its users, especially elderly, on their everyday needs. In this paper, we propose using convolutional neural networks (CNN) for classifying audio streams. In contrast to AAL systems using traditional machine learning, our solution is capable of learning and inferring activities in an end-to-end manner. To demonstrate the system, we developed a wireless sensor network composed of Raspberry Pi boards with microphones as nodes. The audio classification system results to an accuracy of 83.79% using a parallel network for the Urban8k dataset, extracting constant-Q transform (CQT) features as system inputs. The overall system is scalabale and flexible in terms of the number of nodes, hence it is applicable on wide areas where assisted living applications are utilized.