A Machine Learning Based Victim's Scream Detection System for Burning Sites Using an Autonomous Embedded System Vehicle

Fire incidents are responsible for severe damage and thousands of deaths every year all over the world. Extreme temperatures, low visibility, toxic gases, and unknown locations of victims create difficulties and delays in rescue operations, escalating the risk of injury or death. It is time-critical to detect the victims trapped inside the burning sites for facilitating the rescue operations. Since human beings tend to scream for help in emergency situations, this type of audio events can play a crucial role to detect victims trapped inside burning sites. The information regarding victim’s presence can help the firefighters to make a faster and a safer rescue plan. This research work presents an audio-based automated system for victim’s scream detection in fire emergencies, through the investigation of three machine learning (ML) approaches: Support Vector Machines (SVM), Long Short-Term Memory (LSTM) and transfer learning with Yet Another Mobile Network (YAMNet). The performance of these three techniques has been evaluated based on a variety of performance metrics. The models with top performance on scream detection were implemented in an Autonomous Embedded System Vehicle (AESV). This research work also presents the performance analysis of these models in field testing. The main objective of this thesis is to develop an autonomous victim detection system in burning sites from scream sounds of victim(s) for effective rescue operation.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Electrical Engineering

Grantor

Texas State University

Year dc:date.issued

2021

Author dc:creator

• Saeed, Fairuz Samiha

Advisor dc:contributor.advisor

• Valles, Damian

Committee members dc:contributor.committeemember

• Viswanathan, Vishu
• Stapleton, William

• burning sites
• screams
• firefighters
• machine learning
• AESV
• victim detections

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10877/17841

OAI identifier oai:identifier

oai:digital.library.txst.edu:10877/17841