Real time end-to-end glass break detection system using LSTM deep recurrent neural network

Abstract

Presently, glass windows in commercial and residential buildings are very popular. While glass has its benefits, it is also disposed to security risks. Almost all glass break detectors use a pre-determined frequency of breaking glass sound and vibration threshold signals of a pane to determine whether or not breakage has occurred. However, sounds such as thunder sounds, shouting, gunshot, hitting objects are similar in frequency and threshold value to glass breaking sounds events, and may consequently cause false positives in detection in the alarm system. The aim of this paper is to propose a new design for a glass break detection system using LSTM deep recurrent neural networks in an end to-end approach to reduce false positive alarm of state of the art glass break detectors. We utilized raw wave audio data to detect a glass break detection event in End-to-End learning approach. The key benefit of End-to-End learning is avoiding the need of hand-crafted audio features. To address the issue of a vanishing gradient and exploding gradient problem in conventional recurrent neural networks, this paper proposed deep long short term memory (LSTM) recurrent neural network to handle the sequence of the input audio data. As a real time detection result, the proposed glass break detection approach has a clear advantage over the conventional glass break detection system, as it yields significantly higher precision accuracy (99.999988 %) and suffers less from environmental noise that might cause a false alarm.