# An Initial Machine Learning-Based Victim's Scream Detection Analysis for Burning Sites
## 5 Conclusions  
A structure fire is a critical global issue that causes severe damage and thousands of casualties every year. Hazards such as toxic gasses, extreme temperatures, and victims' unknown locations make the rescue operation challenging and placing firefighters' safety at stake. Firefighting techniques are continually evolving due to technological advancements. Unfortunately, the number of deaths per year shows an increasing trend, which is alarming. It is vital to provide firefighters with information about the victim's presence to increase the efficiency of their rescue effort. Human nature tends to scream out of fear in emergencies. Therefore, screams can be used to detect victims in burning sites.  
This research aims to detect victims trapped in fire emergencies from their screams, using a machine learning model and an autonomous vehicle that can travel around the fire location. The development of the ML model involves multiple stages, including custom dataset building, audio pre-processing, feature selection and extraction, and finally, ML-based classification. We implemented two models (SVM, LSTM) in the classifier's role and evaluated their performance for scream detection. Our further goal is to implement a computationally efficient model.  
We have reported promising results with both models, with SVM slightly overperforming the LSTM network with 98% accurately predicted scream. Moreover, the effect of SNR has a lower impact on SVM. Additionally, computational time of SVM was much shorter than for LSTM. These factors make the SVM model be a better model for real-time implementation on an embedded computational platform. We are in the process of implementing the SVM model in real-time application on the Jetson Nano platform. Currently, the autonomous vehicle AESV does not have a microphone attached. Therefore, we intend to attach a microphone for collecting audio data samples and test the model running in Jetson Nano on these samples. The system will incorporate ROS to communicate and transfer AESV classification decisions to the base station if the system detects a scream.  
In this paper, we documented the results obtained from a balanced dataset. However, screams are less common audio events to occur in real situations. We will investigate this further to make the proposed model work in an unbalanced dataset. In future work, research is warranted on the localization of victims using a microphone array and applying the time difference of arrival (TDOA) technique [64]. If appropriately implemented, this solution can play an essential role in the rescue operation, saving valuable time and contributing to physical safety for the firefighters.  
Conceptualization, D.V. and F.S.S.; methodology, F.S.S.; software, F.S.S.; validation, F.S.S., V.V., A.A.B. and D.V.; formal analysis, D.V., V.V. and A.A.B.; investigation, F.S.S. and V.V. resources, D.V. data curation, F.S.S.; writing--original draft preparation, F.S.S.; writing--review and editing, A.A.B., V.V. and D.V.; visualization, F.S.S.; supervision, D.V. and V.V.; project administration, D.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Authors** & **Architecture** & **Accuracy** \\ \hline [PERSON] et al. [29] & GMM & 93\% \\ [PERSON] et al. [30] & SVM & 92\% \\ [PERSON] et al. [25] & DNN & 93.2\% \\ [PERSON] et al. [37] & LSTM & 93.1\% \\ [PERSON] et al. [14] & Feed forward neural network & 85.7\% \\ [PERSON] et al. [27] & GMM & 99\% \\ [PERSON] et al. [32] & SVM & 93.16\% \\ [PERSON] et al. & LSTM & 95\% \\ [PERSON], [PERSON] et al. & SVM & 98\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparing the result with previous research work in scream detection.