BP19: An Accurate Audio Violence Detection Model Based On One-Dimensional Binary Pattern

Authors : Arif Metehan YILDIZ, Tuğçe KELEŞ, Kübra YILDIRIM, Sengul DOGAN, Türker TUNCER

Pages : 215-222

Doi:10.55525/tjst.1244759

View : 50 | Download : 16

Publication Date : 2023-03-29

Article Type : Research Paper

Abstract :Audio violence detection insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(BP); has been considered to extract features. Moreover, we have applied tunable q-factor wavelet transform insert ignore into journalissuearticles values(TQWT); to generate features at both frequency and space domains. In the feature selection phase, we have applied to iterative neighborhood component analysis insert ignore into journalissuearticles values(INCA); and the selected features have been classified by deploying the optimized support vector machine insert ignore into journalissuearticles values(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. .
Keywords : Sesten şiddet tespiti, özellik mühendisliği, makine öğrenimi, ses adli bilişimi

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