Identification and Categorization of Unusual Internet of Vehicles Events in Noisy Audio

The volume of multimedia data produced by various smart devices has increased dramatically with the advent of new digital technologies, including in the Internet of Vehicles (IoV). It has become more difficult to extract valuable insights from multimedia data due to several challenges during data analysis. The main problem is the need to quickly and precisely identify abnormalities in multimedia data. This research presents an unusual occurrence of the audio forensics database named UOAFDB and a practical method for identifying and categorizing unusual occurrences in audio files. To study the detection of abnormal audio and the classification of rare sound (e.g., car crash-machine gun, explosion) events for audio forensics, we construct a large audio dataset containing ten rare special events (anomalies) with 15 different background environmental settings (e.g., beach, restaurant, and train). The suggested method determines the optimal amount of features using the best feature extraction methodology available by extracting Mel-frequency cepstral coefficients (MFCCs) features from the audio signals of the newly formed dataset. Modern deep learning algorithms use these features as input to assess performance. Additionally, we apply deep learning methods to the most recent and best available dataset and obtain promising outcomes. The experimental findings demonstrate promising results on the UOAFDB dataset.