DeepCrashzam-Dataset

Script to create the crash sound dataset. Sounds are recorded inside the car cabin with dashcam or smartphone.

Categories:

• Crash
• Driving
• Tire skidding
• Horn
• Harsh acceleration
• People talking
• People screaming
• Music
• Pothole
• Meteo (strong rain/hail)
• Police siren
• Ambulance siren
• Firetruck siren

Requirements

In order to run this script, you should have already installed:

• youtube-dl
• sox

Files

• dasetCreation.sh: the main script
• youtube_IDs.csv: list of youtube videos.
• labels: folder with txt files, each with the annotation [start time] [end time] [class]

Script running

$ bash datasetCreation.sh ./labels/ ./output

This will create a output folder with a sub-folder per category, including wav files.

Contribution/Extension

1. Add the new {video_youtube_id} and relative title to the youtube_IDs.csv file.
2. Use Audacity to annotate the file.
   • Open the file in Audacity
   • Right click on the track -> split stereo to mono and delete one of the two tracks
   • Track menu -> add new track- > label track
   • Select part and them command+b (or ctrl+b) to add the label
   • Edit menu -> Labels -> edit labels -> check and export into a file named {video_youtube_id}.txt
   • Finally to save clips: File menu -> Export -> Export Multiple. Option: split files based on Labels and Name files numbering before Label/track name. At this point file name has this convention {video_youtube_id}_{2_digits_counter}-category (e.g., HRamesEI1Iw_39-ambulance.wav)
3. Save the annotation in a video_youtube_id.txt file in the labels folder.

Acknowledgement

If you use this dataset, please cite it:

@InProceedings{10.1007/978-3-030-26633-2_8,
author="Sammarco, Matteo and Detyniecki, Marcin",
editor="Donnellan, Brian and Klein, Cornel and Helfert, Markus and Gusikhin, Oleg",
title="Car Accident Detection and Reconstruction Through Sound Analysis with Crashzam",
booktitle="Smart Cities, Green Technologies and Intelligent Transport Systems",
year="2019",
publisher="Springer International Publishing",
pages="159--180",
isbn="978-3-030-26633-2"
}