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

Arif Metehan Yıldız; Tuğçe Keleş; Kübra Yıldırım; Sengul Dogan; Türker Tuncer

Article Details

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

Authors:

Arif Metehan Yıldız | Tuğçe Keleş | Kübra Yıldırım | Sengul Dogan | Türker Tuncer

Publisher:

Fırat University

Journal:

Turkish Journal of Science and Technology Vol.18 Issue 1, pp.215-222

Document Type:

Article

Publication Date:

3/2023

Document Language:

English

Document ID (Org No.):

2001285855

pISSN:

1308-9080

Keywords:

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. .

 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. .

 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. .

 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. .

Article Details

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

Authors:

Arif Metehan Yıldız | Tuğçe Keleş | Kübra Yıldırım | Sengul Dogan | Türker Tuncer

Publisher:

Fırat University

Journal:

Turkish Journal of Science and Technology Vol.18 Issue 1, pp.215-222

Document Type:

Article

Publication Date:

3/2023

Document Language:

English

Document ID (Org No.):

2001285855

pISSN:

1308-9080

Audio violence detection

feature engineering

machine learning

sound forensics

Sesten şiddet tespiti

özellik mühendisliği

makine öğrenimi

ses adli bilişimi.

Issues

Article Details

Authors: Arif Metehan Yıldız | Tuğçe Keleş | Kübra Yıldırım | Sengul Dogan | Türker Tuncer

Publisher: Fırat University

Journal: Turkish Journal of Science and Technology Vol.18 Issue 1, pp.215-222

Document Type: Article

Publication Date: 3/2023

Document Language: English

Document ID (Org No.): 2001285855

pISSN: 1308-9080

Issues

Authors:

Arif Metehan Yıldız | Tuğçe Keleş | Kübra Yıldırım | Sengul Dogan | Türker Tuncer

Publisher:

Fırat University

Journal:

Turkish Journal of Science and Technology Vol.18 Issue 1, pp.215-222

Document Type:

Article

Publication Date:

3/2023

Document Language:

English

Document ID (Org No.):

2001285855

pISSN:

1308-9080