Audio Events Detection in Noisy Embedded Railway Environments

  title={Audio Events Detection in Noisy Embedded Railway Environments},
  author={Tony Marteau and Sitou Afanou and David Sodoyer and S{\'e}bastien Ambellouis and Fouzia Elbahhar},
  booktitle={EDCC Workshops},
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… 


Audio Surveillance of Roads: A System for Detecting Anomalous Sounds
A novel method for detecting road accidents by analyzing audio streams to identify hazardous situations such as tire skidding and car crashes is proposed and the obtained results confirm the effectiveness of the proposed approach.
Embedded security system for multi-modal surveillance in a railway carriage
An innovative approach which aims at providing efficient automatic event detection by fusing video and audio analytics and reducing the false alarm rate compared to classical stand-alone video detection is presented.
Sound Event Detection in Domestic Environments with Weakly Labeled Data and Soundscape Synthesis
The paper introduces Domestic Environment Sound Event Detection (DESED) dataset mixing a part of last year dataset and an additional synthetic, strongly labeled, dataset provided this year that’s described more in detail.
Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks
The proposed convolutional recurrent neural network for joint sound event localization and detection (SELD) of multiple overlapping sound events in three-dimensional (3-D) space is generic and applicable to any array structures, robust to unseen DOA values, reverberation, and low SNR scenarios.
Deep neural networks for automatic detection of screams and shouted speech in subway trains
This paper investigated the use of DNNs for automatic scream and shouted speech detection, within the framework of surveillance systems in public transportation, and recorded a database of sounds occurring in subway trains.
Deep Learning for Audio Signal Processing
Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross fertilization between areas.
A Review of Deep Learning Based Methods for Acoustic Scene Classification
This article summarizes and groups existing approaches for data preparation, i.e., feature representations, feature pre-processing, and data augmentation, and for data modeling, i.
Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection
This work combines these two approaches in a convolutional recurrent neural network (CRNN) and applies it on a polyphonic sound event detection task and observes a considerable improvement for four different datasets consisting of everyday sound events.
Sound Event Detection in Multichannel Audio Using Spatial and Harmonic Features
The proposed SED system is compared against the state of the art mono channel method on the development subset of TUT sound events detection 2016 database and the usage of spatial and harmonic features are shown to improve the performance of SED.