Audio Events Detection in Noisy Embedded Railway Environments

Ensuring passengers' safety is one of the daily concerns of railway operators. To do this, various image and sound processing techniques have been proposed in the scientific community. Since the beginning of the 2010s, the development of deep learning made it possible to develop these research areas in the railway field included. Thus, this article deals with the audio events detection task (screams, glass breaks, gunshots, sprays) using deep learning techniques. It describes the methodology for designing a deep learning architecture that is both suitable for audio detection and optimised for embedded railway systems. We will describe how we designed from scratch two CRNN (Convolutional Recurrent Neural Network) for the detection task. And since the creation of a large and varied training database is one of the challenges of deep learning, this article also deals with the innovative methodology used to build a database of audio events in the railway environment. Finally, we will show the very promising results obtained during the experimentation in real of the model.