A siren identification system using deep learning to aid hearing-impaired people

The research presented in this paper is aiming to address the safety issue that hearing-impaired people are facing when it comes to identifying a siren sound. For that purpose, a siren identification system, using deep learning, was designed, built, and tested. The system consists of a convolutional neural network that used image recognition techniques to identify the presence of a siren by converting the incoming sound into spectrograms. The problem with the lack of datasets for the training of the network was addressed by generating the appropriate data using a variety of siren sounds mixed with relevant environmental noise. A hardware interface was also developed to communicate the detection of a siren with the user, using visual methods. After training the model, the system was extensively tested using realistic scenarios to assess its performance For the siren sounds that were used for training, the system achieved an accuracy of 98 per cent. For real-world siren sounds, recorded in the central streets of London, the system achieved an accuracy of 91 per cent. When it comes to the operation of the system in noisy environments, the tests showed that the system can identify the presence of siren when this is at a sound level of up to -6 db below the background noise. These results prove that the proposed system can be used as a base for the design of a siren-identification application for hearing-impaired people.