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With the emergence of new digital technologies, a significant surge has been seen in the volume of multimedia data generated from various smart devices. Several challenges for data analysis have emerged to extract useful information from multimedia data. One such challenge is the early and accurate detection of anomalies in multimedia data. This study proposes an efficient technique for anomaly detection and classification of rare events in audio data. In this paper, we develop a vast audio dataset containing seven different rare events (anomalies) with 15 different background environmental settings (e.g., beach, restaurant, and train) to focus on both detection of anomalous audio and classification of rare sound (e.g., events—baby cry, gunshots, broken glasses, footsteps) events for audio forensics. The proposed approach uses the supreme feature extraction technique by extracting mel-frequency cepstral coefficients (MFCCs) features from the audio signals of the newly created dataset and selects the minimum number of best-performing features for optimum performance using principal component analysis (PCA). These features are input to state-of-the-art machine learning algorithms for performance analysis. We also apply machine learning algorithms to the state-of-the-art dataset and realize good results. Experimental results reveal that the proposed approach effectively detects all anomalies and superior performance to existing approaches in all environments and cases.

With the continuous rise in ingenious forgery, a wide range of digital audio authentication applications are emerging as a preventive and detective control in real-world circumstances, such as forged evidence, breach of copyright protection, and unauthorized data access. To investigate and verify, this paper presents a novel automatic authentication system that differentiates between the forged and original audio. The design philosophy of the proposed system is primarily based on three psychoacoustic principles of hearing, which are implemented to simulate the human sound perception system. Moreover, the proposed system is able to classify between the audio of different environments recorded with the same microphone. To authenticate the audio and environment classification, the computed features based on the psychoacoustic principles of hearing are dangled to the Gaussian mixture model to make automatic decisions. It is worth mentioning that the proposed system authenticates an unknown speaker irrespective of the audio content i.e., independent of narrator and text. To evaluate the performance of the proposed system, audios in multi-environments are forged in such a way that a human cannot recognize them. Subjective evaluation by three human evaluators is performed to verify the quality of the generated forged audio. The proposed system provides a classification accuracy of 99.2% ± 2.6. Furthermore, the obtained accuracy for the other scenarios, such as text-dependent and text-independent audio authentication, is 100% by using the proposed system.

The transmission frequency of power grids, i.e., electric network frequency (ENF), has become a common criterion to authenticate audio recordings during the past decade, drawing much attention from both the academic researchers and law enforcement agencies world widely. The properties of ENF enable forensic applications such as audio evidence timestamp verification and tampering detection. In this paper, based on a general review of existing works, we discuss several important practical problems and facts that have drawn less research attention or have not been formally studied, including ENF detection problems, limitations of the ENF-based tampering detection systems, and the difficulties in the ENF analysis. During ENF detection, the challenges come from not only the noise and the interference, but also the fact that audio recordings without captured ENF can still have signal components in the frequency band of interest (false positive). In ENF-based tampering detection systems, the weakness of commonly used assumptions and the limitations of several existing solutions are discussed. In addition, we reveal that in the most intensively studied ENF-based audio evidence timestamp verification, many works aiming at improving ENF estimation could only produce marginal performance improvement, while the main problems due to noise and interference remain open. All these analysis and discussions are related by a proposed big picture of ENF-based audio authentication systems. After that, we also investigate the strategies to design more reliable audio authentication systems based on the ENF, which consists of a series of research and investigation works.