# An Initial Machine Learning-Based Victim's Scream Detection Analysis for Burning Sites
## 3 Methodology
### Custom Dataset  
Due to the unavailability of a dataset focused on audio events appearing in burning sites, we have developed our own custom dataset by collecting audio samples from multiple audio-based datasets. We have considered five types of audio event that may appear in fire scenario. These are the five classes for our dataset labeled as'scream', 'glass breaking', 'background', 'alarms', and 'conversations', where'scream' is our class of interest for this specific application. The'scream' category contains both male and female screams. Audio samples of scream and glass breaking have been collected from MIVIA audio events dataset [42]. MIVIA also has another surveillance application: gunshots; however, we discarded this event in our custom dataset due to less relevance with burning site noises. Audio samples from background have been collected from CHiME [43]. This event includes audio recordings of human speakers, television, and household appliances in a domestic environment. Alarm samples have been collected from open access 'freesound' [44] online dataset, which contains audio recordings of fire alarms, emergency alarms, siren and smoke detector alarms. Alarms and screams both have high pitch and intensity or loudness; therefore, we included alarms as a class so that the model learns to differentiate between these two audio events. Lastly, we have collected human conversation (in telephone, hallway, office and canteen) audio samples from CHiME and 'Sound Privacy' dataset [45]. Figure 3: Architectural building blocks for the proposed ML model. The intention behind adding this class was to prevent the ML model operating in the AESV from mistaking normal conversation from either the firefighters or people outside of the building for people still trapped inside. These collected samples are clean files with no added background noise. Table 1 lists the number of collected audio files for each category and their source. These audio files have different durations ranging from 1 s to 3 s.  
To mimic the actual fire scene, we have added complex background noise (burning sounds, fire sounds, siren, telephone ringing) with the clean dataset to create a noise-added dataset. These noise files have been added to the clean signal files at nine different signal-to-noise ratio (SNR) levels (\(-10\) dB, \(-5\) dB, \(0\) dB, \(5\) dB, \(10\) dB, \(15\) dB, \(20\) dB, \(25\) dB, \(30\) dB). Here, SNR corresponds to the ratio between the power of clean file versus the power of the noise file. Higher value of SNR corresponds to cleaner audio signal and lower SNR corresponds to noisier audio signal. The approach of varying SNR levels also covers the aspect of audio events occurring at various distances. Audio file with higher SNR appears as the source that is closer to the recording device, whereas audio file with lower SNR appears as the source that is farther from the recording device. We included different SNR conditions to develop a model that is robust to noise. To create the noise-added file, we first randomly chose a background noise file and chopped it equal to the length of the clean file. Then, the power is calculated for both clean and noise file. From this power values, scale factor in signal is calculated for the desired SNR (in dB) level using Equation (1). \[\text{Scale factor},\ k=\sqrt{\frac{P_{CLEAN}}{P_{NOISE}}}\cdot 10^{\frac{- \text{SNR}}{10}} \tag{1}\]  
Here, \(P_{CLEAN}\) and \(P_{NOISE}\) refers to power of clean and noise signals, respectively. The noise is then superimposed to the clean file using Equation (2). \[\text{Noise added signal = Clean signal + }k.\ \text{Noise signal} \tag{2}\]  
All the files from the clean dataset have been added with background noise with nine levels of SNR. The noise addition was done in MATLAB. Table 1 shows the final counts of noise added samples for each class. Figure 4 shows the spectrograms of three audio samples from the'scream' class in our custom dataset. The left spectrogram corresponds to a clean stream file (SNR 30 dB).