| Title | Security Threat Sounds Classification Using Neural Network |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Agarwal, Shivam, Khatter, Kiran, Relan, Devanjali |
| Conference Name | 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom) |
| Date Published | mar |
| Keywords | artificial neural network, Artificial neural networks, Collaboration, convolutional neural network, convolutional neural networks, cyber physical systems, event detection, Mel frequency cepstral coefficient, Metrics, Neural Network Security, Neural networks, policy-based governance, pubcrawl, resilience, Resiliency, security, Sound recognition and classification, surveillance, Training, Training data |
| Abstract | Sound plays a key role in human life and therefore sound recognition system has a great future ahead. Sound classification and identification system has many applications such as system for personal security, critical surveillance, etc. The main aim of this paper is to detect and classify the security sound event using the surveillance camera systems with integrated microphone based on the generated spectrograms of the sounds. This will enable to track security events in cases of emergencies. The goal is to propose a security system to accurately detect sound events and make a better security sound event detection system. We propose to use a convolutional neural network (CNN) to design the security sound detection system to detect a security event with minimal sound. We used the spectrogram images to train the CNN. The neural network was trained using different security sounds data which was then used to detect security sound events during testing phase. We used two datasets for our experiment training and testing datasets. Both the datasets contain 3 different sound events (glass break, gun shots and smoke alarms) to train and test the model, respectively. The proposed system yields the good accuracy for the sound event detection even with minimum available sound data. The designed system achieved accuracy was 92% and 90% using CNN on training dataset and testing dataset. We conclude that the proposed sound classification framework which using the spectrogram images of sounds can be used efficiently to develop the sound classification and recognition systems. |
| DOI | 10.1109/INDIACom51348.2021.00122 |
| Citation Key | agarwal_security_2021 |
Security Threat Sounds Classification Using Neural Network