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

Year 2023, Volume: 18 Issue: 1, 215 - 222, 29.03.2023
https://doi.org/10.55525/tjst.1244759

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.
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Supporting Institution

yok

Project Number

yok

Thanks

I would like to thank all the authors who contributed to science by working in the field of Digital forensic while writing this article, and my advisor Turker Tuncer and head of our department Sengul Dogan who contributed to the creation of the article.

References

  • Anwar A, Kanjo E, Anderez DO. DeepSafety: Multi-level Audio-Text Feature Extraction and Fusion Approach for Violence Detection in Conversations. arXiv e-prints 2022; arXiv:2206.11822.
  • Baumeister RF, Bushman BJ. Emotions and aggressiveness, International handbook of violence research. Heitmeyer W, Hagan J, editors. Springer Dordrecht, 2003; 479–493.
  • Allen T, Novak SA, Bench LL. Patterns of injuries: accident or abuse. Violence Against Wom 2007;13(8):802–16.
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  • Davidson T, Warmsley D, Macy M, Weber I. Automated hate speech detection and the problem of offensive language. In: Proceedings of the international AAAI conference on web and social media; 15-18 May 2017; Montreal, Quebec, Canada. pp. 512–515.
  • Bhavan A, Chauhan P, Shah RR, others. Bagged support vector machines for emotion recognition from speech. Knowl-Based Syst 2019; 184:104886.
  • Atmaja BT, Akagi M. Speech emotion recognition based on speech segment using LSTM with attention model. In: 2019 IEEE International Conference on Signals and Systems (ICSigSys); 16 – 18 July 2019; Bandung, Indonesia. pp. 40–44.
  • Li Y, Zhao T, Kawahara T, others. Improved End-to-End Speech Emotion Recognition Using Self Attention Mechanism and Multitask Learning. In: Interspeech. 15-19 September 2019; Graz, Austria. pp. 2803–2807.
  • Hajarolasvadi N, Demirel H. Deep facial emotion recognition in video using eigenframes. IET Image Process 2020; 14(14):3536–3546.
  • Hu M, Wang H, Wang X, Yang J, Wang R. Video facial emotion recognition based on local enhanced motion history image and CNN-CTSLSTM networks. J Vis Commun Image R 2019; 59:176–185.
  • Du Z, Wu S, Huang D, Li W, Wang Y. Spatio-temporal encoder-decoder fully convolutional network for video-based dimensional emotion recognition. IEEE T Affect Comput 2019; 12(3):565–578.
  • Plaza-del-Arco FM, Mart\’\in-Valdivia MT, Urena-Lopez LA, Mitkov R. Improved emotion recognition in Spanish social media through incorporation of lexical knowledge. Future Gener Comp Sy 2020; 110:1000–1008.
  • Yang CT, Chen YL. Dacnn: Dynamic weighted attention with multi-channel convolutional neural network for emotion recognition. In: 2020 21st IEEE international conference on mobile data management (MDM); 30 June - 3 July 2020; Versailles, France. pp. 316–321.
  • Batbaatar E, Li M, Ryu KH. Semantic-emotion neural network for emotion recognition from text. IEEE access 2019; 7:111866–111878.
  • Abdullah NSD, Zolkepli IA. Sentiment analysis of online crowd input towards brand provocation in Facebook, Twitter, and Instagram. In: Proceedings of the International Conference on Big Data and Internet of Thing; 20 - 22 December 2017; London, United Kingdom. pp. 67–74.
  • Kang Y, Cai Z, Tan CW, Huang Q, Liu H. Natural language processing (NLP) in management research: A literature review. Journal of Management Analytics 2020; 7(2):139–172.
  • Sharma HK, Kshitiz K, others. Nlp and machine learning techniques for detecting insulting comments on social networking platforms. In: 2018 International Conference on Advances in Computing and Communication Engineering (ICACCE); 22-23 June 2018; Paris, France. pp. 265–272.
  • Mossie Z, Wang JH. Vulnerable community identification using hate speech detection on social media. Inf Process Manag 2020; 57(3):102087.
  • T. Giannakopoulos, D. Kosmopoulos, A. Aristidou, and S. Theodoridis, Violence content classification using audio features, in Advances in Artificial Intelligence: 4th Helenic Conference on AI, SETN 2006; 18-20 May 2006; Heraklion, Crete, Greece. pp. 502-507.
  • Khanafseh M, Qatawneh M, Almobaideen W. A survey of various frameworks and solutions in all branches of digital forensics with a focus on cloud forensics. Int J Adv Comput Sci Appl 2019; 10(8).
  • Subasi A, Tuncer T, Dogan S, Tanko D. Local binary pattern based feature extraction and machine learning for epileptic seizure prediction and detection. Modelling and Analysis of Active Biopotential Signals in Healthcare Volume 2 , Bristol, UK : IOP Publishing, 2020; pp. 6-1 to 6-31.
  • Zeng W, Yuan J, Yuan C, Wang Q, Liu F, Wang Y. A new approach for the detection of abnormal heart sound signals using TQWT, VMD and neural networks. Artif Intell Rev 2021; 54(3):1613–47.
  • Tuncer T, Ertam F. Neighborhood component analysis and reliefF based survival recognition methods for Hepatocellular carcinoma. Physica A: Statistical Mechanics and its Applications 2020; 540:123143.
  • Keerthi SS, Shevade SK, Bhattacharyya C, Murthy KRK. A fast iterative nearest point algorithm for support vector machine classifier design. IEEE T Neural Networ 2000; 11(1):124–36.
  • Wu J, Chen XY, Zhang H, Xiong LD, Lei H, Deng SH. Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimizationb. Journal of Electronic Science and Technology. 2019; 17(1):26–40.
  • Primus P, Haunschmid V, Praher P, Widmer G. Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier Examples. In:Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020); 2–3 November 2020; Tokyo, Japan. pp. 170-174.
  • Dogan S, Barua PD, Kutlu H, Baygin M, Fujita H, Tuncer T, et al. Automated accurate fire detection system using ensemble pretrained residual network. Expert Syst Appl 2022; 203:117407.

BP19: Tek Boyutlu İkili Modele Dayalı Doğru Bir Sesli Şiddet Tespit Modeli

Year 2023, Volume: 18 Issue: 1, 215 - 222, 29.03.2023
https://doi.org/10.55525/tjst.1244759

Abstract

Project Number

yok

References

  • Anwar A, Kanjo E, Anderez DO. DeepSafety: Multi-level Audio-Text Feature Extraction and Fusion Approach for Violence Detection in Conversations. arXiv e-prints 2022; arXiv:2206.11822.
  • Baumeister RF, Bushman BJ. Emotions and aggressiveness, International handbook of violence research. Heitmeyer W, Hagan J, editors. Springer Dordrecht, 2003; 479–493.
  • Allen T, Novak SA, Bench LL. Patterns of injuries: accident or abuse. Violence Against Wom 2007;13(8):802–16.
  • Bulut M, Aslan R, Arslantaş H. Kabul Edilmemesi Gereken Toplumsal Bir Gerçek: Yakn Partner Şiddeti. Sakarya Tıp Dergisi 2020; 10(2):334–347.
  • Davidson T, Warmsley D, Macy M, Weber I. Automated hate speech detection and the problem of offensive language. In: Proceedings of the international AAAI conference on web and social media; 15-18 May 2017; Montreal, Quebec, Canada. pp. 512–515.
  • Bhavan A, Chauhan P, Shah RR, others. Bagged support vector machines for emotion recognition from speech. Knowl-Based Syst 2019; 184:104886.
  • Atmaja BT, Akagi M. Speech emotion recognition based on speech segment using LSTM with attention model. In: 2019 IEEE International Conference on Signals and Systems (ICSigSys); 16 – 18 July 2019; Bandung, Indonesia. pp. 40–44.
  • Li Y, Zhao T, Kawahara T, others. Improved End-to-End Speech Emotion Recognition Using Self Attention Mechanism and Multitask Learning. In: Interspeech. 15-19 September 2019; Graz, Austria. pp. 2803–2807.
  • Hajarolasvadi N, Demirel H. Deep facial emotion recognition in video using eigenframes. IET Image Process 2020; 14(14):3536–3546.
  • Hu M, Wang H, Wang X, Yang J, Wang R. Video facial emotion recognition based on local enhanced motion history image and CNN-CTSLSTM networks. J Vis Commun Image R 2019; 59:176–185.
  • Du Z, Wu S, Huang D, Li W, Wang Y. Spatio-temporal encoder-decoder fully convolutional network for video-based dimensional emotion recognition. IEEE T Affect Comput 2019; 12(3):565–578.
  • Plaza-del-Arco FM, Mart\’\in-Valdivia MT, Urena-Lopez LA, Mitkov R. Improved emotion recognition in Spanish social media through incorporation of lexical knowledge. Future Gener Comp Sy 2020; 110:1000–1008.
  • Yang CT, Chen YL. Dacnn: Dynamic weighted attention with multi-channel convolutional neural network for emotion recognition. In: 2020 21st IEEE international conference on mobile data management (MDM); 30 June - 3 July 2020; Versailles, France. pp. 316–321.
  • Batbaatar E, Li M, Ryu KH. Semantic-emotion neural network for emotion recognition from text. IEEE access 2019; 7:111866–111878.
  • Abdullah NSD, Zolkepli IA. Sentiment analysis of online crowd input towards brand provocation in Facebook, Twitter, and Instagram. In: Proceedings of the International Conference on Big Data and Internet of Thing; 20 - 22 December 2017; London, United Kingdom. pp. 67–74.
  • Kang Y, Cai Z, Tan CW, Huang Q, Liu H. Natural language processing (NLP) in management research: A literature review. Journal of Management Analytics 2020; 7(2):139–172.
  • Sharma HK, Kshitiz K, others. Nlp and machine learning techniques for detecting insulting comments on social networking platforms. In: 2018 International Conference on Advances in Computing and Communication Engineering (ICACCE); 22-23 June 2018; Paris, France. pp. 265–272.
  • Mossie Z, Wang JH. Vulnerable community identification using hate speech detection on social media. Inf Process Manag 2020; 57(3):102087.
  • T. Giannakopoulos, D. Kosmopoulos, A. Aristidou, and S. Theodoridis, Violence content classification using audio features, in Advances in Artificial Intelligence: 4th Helenic Conference on AI, SETN 2006; 18-20 May 2006; Heraklion, Crete, Greece. pp. 502-507.
  • Khanafseh M, Qatawneh M, Almobaideen W. A survey of various frameworks and solutions in all branches of digital forensics with a focus on cloud forensics. Int J Adv Comput Sci Appl 2019; 10(8).
  • Subasi A, Tuncer T, Dogan S, Tanko D. Local binary pattern based feature extraction and machine learning for epileptic seizure prediction and detection. Modelling and Analysis of Active Biopotential Signals in Healthcare Volume 2 , Bristol, UK : IOP Publishing, 2020; pp. 6-1 to 6-31.
  • Zeng W, Yuan J, Yuan C, Wang Q, Liu F, Wang Y. A new approach for the detection of abnormal heart sound signals using TQWT, VMD and neural networks. Artif Intell Rev 2021; 54(3):1613–47.
  • Tuncer T, Ertam F. Neighborhood component analysis and reliefF based survival recognition methods for Hepatocellular carcinoma. Physica A: Statistical Mechanics and its Applications 2020; 540:123143.
  • Keerthi SS, Shevade SK, Bhattacharyya C, Murthy KRK. A fast iterative nearest point algorithm for support vector machine classifier design. IEEE T Neural Networ 2000; 11(1):124–36.
  • Wu J, Chen XY, Zhang H, Xiong LD, Lei H, Deng SH. Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimizationb. Journal of Electronic Science and Technology. 2019; 17(1):26–40.
  • Primus P, Haunschmid V, Praher P, Widmer G. Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier Examples. In:Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020); 2–3 November 2020; Tokyo, Japan. pp. 170-174.
  • Dogan S, Barua PD, Kutlu H, Baygin M, Fujita H, Tuncer T, et al. Automated accurate fire detection system using ensemble pretrained residual network. Expert Syst Appl 2022; 203:117407.
There are 27 citations in total.

Details

Primary Language English
Journal Section TJST
Authors

Arif Metehan Yıldız 0000-0003-0451-8600

Tuğçe Keleş 0000-0003-0131-2826

Kübra Yıldırım 0000-0002-4738-2777

Sengul Dogan 0000-0001-9677-5684

Türker Tuncer 0000-0002-5126-6445

Project Number yok
Publication Date March 29, 2023
Submission Date January 31, 2023
Published in Issue Year 2023 Volume: 18 Issue: 1

Cite

APA Yıldız, A. M., Keleş, T., Yıldırım, K., Dogan, S., et al. (2023). BP19: An Accurate Audio Violence Detection Model Based On One-Dimensional Binary Pattern. Turkish Journal of Science and Technology, 18(1), 215-222. https://doi.org/10.55525/tjst.1244759
AMA Yıldız AM, Keleş T, Yıldırım K, Dogan S, Tuncer T. BP19: An Accurate Audio Violence Detection Model Based On One-Dimensional Binary Pattern. TJST. March 2023;18(1):215-222. doi:10.55525/tjst.1244759
Chicago Yıldız, Arif Metehan, Tuğçe Keleş, Kübra Yıldırım, Sengul Dogan, and Türker Tuncer. “BP19: An Accurate Audio Violence Detection Model Based On One-Dimensional Binary Pattern”. Turkish Journal of Science and Technology 18, no. 1 (March 2023): 215-22. https://doi.org/10.55525/tjst.1244759.
EndNote Yıldız AM, Keleş T, Yıldırım K, Dogan S, Tuncer T (March 1, 2023) BP19: An Accurate Audio Violence Detection Model Based On One-Dimensional Binary Pattern. Turkish Journal of Science and Technology 18 1 215–222.
IEEE A. M. Yıldız, T. Keleş, K. Yıldırım, S. Dogan, and T. Tuncer, “BP19: An Accurate Audio Violence Detection Model Based On One-Dimensional Binary Pattern”, TJST, vol. 18, no. 1, pp. 215–222, 2023, doi: 10.55525/tjst.1244759.
ISNAD Yıldız, Arif Metehan et al. “BP19: An Accurate Audio Violence Detection Model Based On One-Dimensional Binary Pattern”. Turkish Journal of Science and Technology 18/1 (March 2023), 215-222. https://doi.org/10.55525/tjst.1244759.
JAMA Yıldız AM, Keleş T, Yıldırım K, Dogan S, Tuncer T. BP19: An Accurate Audio Violence Detection Model Based On One-Dimensional Binary Pattern. TJST. 2023;18:215–222.
MLA Yıldız, Arif Metehan et al. “BP19: An Accurate Audio Violence Detection Model Based On One-Dimensional Binary Pattern”. Turkish Journal of Science and Technology, vol. 18, no. 1, 2023, pp. 215-22, doi:10.55525/tjst.1244759.
Vancouver Yıldız AM, Keleş T, Yıldırım K, Dogan S, Tuncer T. BP19: An Accurate Audio Violence Detection Model Based On One-Dimensional Binary Pattern. TJST. 2023;18(1):215-22.