AI Summary & Key Takeaways
AI InsightConclusions: The calculated results clearly demonstrated that feature engineering is the success solution for violence detection using audios. .
📌 3 Key Takeaways
Abstract
Audio violence detection (AVD) is a hot-topic research area for sound forensics but there are limited AVD researches in the literature. Our primary objective is to contribute to sound forensics. Therefore, we collected a new audio dataset and proposed a binary pattern-based classification algorithm. Materials and method: In the first stage, a new AVD dataset was collected. This dataset contains 301 sounds with two classes and these classes are violence and nonviolence. We have used this dataset as a test-bed. A feature engineering model has been presented in this research. One-dimensional binary pattern (BP) has been considered to extract features. Moreover, we have applied tunable q-factor wavelet transform (TQWT) to generate features at both frequency and space domains. In the feature selection phase, we have applied to iterative neighborhood component analysis (INCA) and the selected features have been classified by deploying the optimized support vector machine (SVM) classifier. Results: Our model achieved 97.01% classification accuracy on the used dataset with 10-fold cross-validation. Conclusions: The calculated results clearly demonstrated that feature engineering is the success solution for violence detection using audios. .