# An Initial Machine Learning-Based Victim's Scream Detection Analysis for Burning Sites
## 1 Introduction  
Structure fires are a common type of incident all over the world. A National Fire Protection Association (NFPA) report says that a fire department in the United States responds to a fire somewhere in the nation every 24 s [1]. Structure fires include fires in eating and drinking places, other entertainment venues, houses of worship, and other places where people congregate, including offices, factories, and various other locations. These fire incidents are responsible for severe damages, thousands of deaths, and injuries. According to the U.S. Fire Administration, the fire departments responded to 1,291,500 fire incidents in 2019. These fires caused USD 14.8 billion of damage, an increase of 74.5% from 2010 and 3704 deaths, 24.1% more than the number of deaths in 2010 [2]. Sadly, the trend in death is going up every year. Evacuating victims from the fire scene is a highly challenging task. The firefighters need to deal with extreme temperatures and low visibility, making it even more challenging to look for victims. Moreover, harmful pollutants such as carbon dioxide and carbon monoxide from burning make the air unsafe for breathing. As the first responders at a burning site, the firefighters' main task is to evacuate victims from the site to safety. The danger level associated with this task is extremely high. Data shows that hundreds of firefighters have lost their lives on duty [3]. Therefore, it is imperative to guide the firefighters with appropriate information that will help them effectively carry out the evacuation process considering the safety of both the victims and firefighters. Autonomous data collection during fire emergencies also saves valuable time by using intelligent firefighting techniques shown in [4, 5]. Researchers continually improve the firefighting technique with the latest technologies where Artificial Intelligence (AI) comes into play [6, 7, 8]. Other research is ongoing on firefighting robots [4, 9] and on improving the quality of sensors and firefighting equipment [10, 11]. The research work in [12] deals with the design of an autonomous embedded system vehicle (AESV)equipped with various sensors, including LiDAR, GPS, and Infrared (IR) camera, which can navigate autonomously into the burning sites, collect sensory data, and then transmit this information to the firefighters in the base station. The research work in [13] presented a deep learning model that can identify victims from IR images in burning sites. However, IR images cannot identify victims trapped inside a confined place. Additionally, a victim's presence cannot be determined from IR images if a particular portion of the body structure is not visible. Since human nature is to stream and shout for help in such emergencies, these audio sources can be used alongside victim detection approach using IR images, which can play a vital role in detecting the victim in fire emergencies. The work in [14] developed a sound identification model for firefighting mobile robots. However, the work did not take the high noise level associated with a burning site into consideration. In this work, a design approach has been developed to increase the efficiency of firefighters' role in a rescue operation by detecting victims in burning sites autonomously through the detection of screams using machine learning (ML) techniques. We aim to efficiently develop a stream detection model in burning sites to work on a computational platform such as the AESV unit. We have developed a custom dataset considering the high noise level in a burning site. We present the performance analysis of two ML architectures with dominant audio features for the robust detection of screams in a fire scenario. The detection model with the highest performance metric will be deployed on the single-board computing platform NVIDIA Jetson Nano [15], the core of the AESV, for real-time implementation.