A Large-Scale Benchmark Dataset for Anomaly Detection and Rare Event Classification for Audio Forensics

Abstract

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-theart 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.

VidAnomalyNet: An Efficient Anomaly Detection in Public Surveillance Videos Through Deep Learning Architectures

“…Approach Techniques Dataset [27] Deep learning DCNN UCSD, CUHK, ShanghaiTech [28] Deep Learning DTM technique UCSD, Mall, UMN and MED [29] Deep Learning Neural Network models Custom dataset [30] Deep Learning SFE technique TUT 2016 [31] Deep Learning and Bio-Inspired CRN along with AntHocNet Custom dataset [32] Deep Learning IIN UCF-Crime, UCSD [33] AI DCNN Custom dataset [34] Deep Learning DCNN methods Seven benchmark datasets [35] Deep Learning DNN UCF-Crime [36] Deep Learning LMNN Custom dataset…”

Weakly Supervised Detection of Baby Cry

“…As a super-set of baby cry detection, audio anomaly detection typically uses unsupervised learning [16]. The work [1] presents a large audio dataset for anomaly detection including baby cry detection. However, even though the term anomaly detection is used, in their detection algorithm, audio files are first cut into small segments then supervised learning is used to classify every segment.…”

Auditory chaos classification in real-world environments

“…It is hard to collect data for anomalies or abnormal events such as gunshots, screams, glass breaking and explosions in the real world as their occurrences are quite rare. To circumvent this problem, to obtain enough data to develop classification models for abnormal events, previous works have leveraged artificially curated datasets created by superimposing the rare events on background noises from different environments (66)(67)(68). Others have collected data by having actors create and enact abnormal situations (40).…”