Detection and Identification of Background Sounds to Improvise Voice Interface in Critical Environments

This work contributes towards building a novel architecture for the real-time detection of background sounds such as ambulance, helicopter, gunshot, and explosions that usually occurs in the medical first responder environment. Further, we also ensured that the proposed architecture is compatible with the existing natural language processing (NLP) systems. In this paper, we use frequency and time domain features for feature extraction and use classifiers like support vector machine (SVM), random forest (RF), and deep neural network (DNN) for classification purpose. Moreover, we also compared the result from the aforementioned machine learning algorithms in order to provide the proposed architecture with the best-classified output. Finally, to verify the validity and effectiveness of the proposed architecture, different samples of background sounds were trained and tested. Our results conclude that proposed architecture is able to detect and identify background sounds with 98% accuracy.