Emergency Siren Recognition in Urban Scenarios: Synthetic Dataset and Deep Learning Models

  title={Emergency Siren Recognition in Urban Scenarios: Synthetic Dataset and Deep Learning Models},
  author={Michela Cantarini and Luca Serafini and Leonardo Gabrielli and Emanuele Principi and Stefano Squartini},

Few-Shot Emergency Siren Detection

Extensive experiments conducted on several recording sensor placements prove that few-shot learning is a reliable technique even in real-world scenarios and gives valuable insights for developing an in-car emergency vehicle detection system.



Acoustic-Based Emergency Vehicle Detection Using Convolutional Neural Networks

This work proposes to develop an automatic detection system that determines whether there are siren sounds from emergency vehicles nearby to alert other vehicles’ drivers to pay attention and could be helpful for not only drivers but also autopilot systems.

Freesound technical demo

This demo wants to introduce Freesound to the multimedia community and show its potential as a research resource.

A Dataset and Taxonomy for Urban Sound Research

A taxonomy of urban sounds and a new dataset, UrbanSound, containing 27 hours of audio with 18.5 hours of annotated sound event occurrences across 10 sound classes are presented.

U-Net: Convolutional Networks for Biomedical Image Segmentation

It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.