# An Initial Machine Learning-Based Victim's Scream Detection Analysis for Burning Sites
## 2 Background
### Scream Detection Architectures  
A scream is a distinctive audio signal that serves the dual purpose of sharpening our focus in the face of a threat and warning others. Research is ongoing on the effect of screams for text-independent speaker recognition systems [20]. Many research works have been conducted on scream detection for surveillance applications in both indoor and outdoor noisy environments [21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32]. Support vector machines (SVM) have been largely used for the detection of screams. The authors in [30] focused on detecting screams using SVM as a classifier for real-time scream detection in a home environment. Their model exhibits a false acceptance rate (FAR) of 7.66%. In [33], a robust scream-sound detection system is presented for a surveillance application using a sound-event partitioning (SEP) method and SVM as a classifier. They reported equal error rates (EER) of 7.24% in detection at a signal-to-noise (SNR) ratio of \(-5\) dB. A two-stage supervised learning-based method with tunable decision parameters for each stage has been proposed in [32] to detect screams and cries in urban environments. This proposed model achieved a detection rate of 93.16% and a FAR of 4.76% at a SNR of 20 dB. SVM has been used in previous research on audio event detection for surveillance applications where scream is a target class. The paper in [34] proposes a low power SVM classifier for audio classification. The performance and power consumption of various acoustic features and SVM kernels have been compared. Results show that the CPU utilization of polynomial SVMs decreases by 28 times without reducing classification accuracy. Authors in [35] discussed a classification approach of speech and non-speech sound where non-speech sounds are further classified into laughs, screams, sneezes, and snores. Their classification techniques included using multivariate adaptive regression splines (MARS) and SVM, and they reported highest classification accuracy of 89.91% with SVM. Research work in [26] proposes a one-class SVM for classifying nine audio events(human screams, gunshots, glass breaks, explosions, door slams, dog barks, and phone rings). Their result shows highest classification accuracy of 92.33%. In recent years, neural networks have become popular for audio event detection. A deep neural network (DNN) model is proposed in [25] for the automatic scream and shouted speech detection within the framework of surveillance systems in the subway to detect emergencies in the early stage. The paper implemented a deep belief network (DBN) followed by DNN to classify the audio sources into four classes: 'Scream', 'Shout', 'Conversation', and 'Noise'. Despite challenges such as a boisterous environment, the model performed well in stream and shout detection with an error rate of 6.2%. The work in [23] focuses on abnormal event detection in indoor activities for an automatic intelligent surveillance system (ISS) by optimizing the Adaboost algorithm. Their proposed algorithm demonstrated improved performance of the detector compared with the conventional Adaboost-based detector. In addition to that, their proposed method was shown to be significantly faster and sufficiently accurate with a minimal false alarm rate compared with the Gaussian mixture model (GMM)-based method. The paper in [36] proposes a region-based convolutional recurrent neural network (R-CRNN) for audio event detection (AED) inspired by Faster-RCNN. The evaluation task was completed on the DCASE'17 dataset for AED with an error rate of 0.18 and an F1-score of 95.5%. In [37], the authors introduce a rare sound event detection system using a combination of 1D convolutional neural network and recurrent neural network (RNN) with long short-term memory units (LSTM). A promising result was reported based on the evaluation of DCASE'17 challenge with an error rate of 0.13 and an F1-Score of 93.1. Research work in [38] has used the LSTM network for enhancing audio surveillance and reported a classification accuracy of 90.7%.