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Journals / Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi / 2021 / Cilt: 8 - Sayı: 14

AN AUTOMATED GUNSHOT AUDIO CLASSIFICATION METHOD BASED ON FINGER PATTERN FEATURE GENERATOR AND ITERATIVE RELIEFF FEATURE SELECTOR

Emrah AYDEMİR
Şengul DOĞAN
Erhan AKBAL
Türker TUNCER
Pages
225–243
Publication Date
2022-10-12
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These results demonstrate that our proposed method can be used in gunshot audio analysis.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Audio forensics applications and methods are very crucial to clarify crimes.
📊
Key Findings & Data:To accelerate audio analysis process and classify audios with high accuracy, machine learning (ML) methods must be used in audio forensics.
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Conclusion & Impact:These results demonstrate that our proposed method can be used in gunshot audio analysis.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Gunshot audio classification · finger pattern · iterative ReliefF

Abstract

Audio forensics applications and methods are very crucial to clarify crimes. To accelerate audio analysis process and classify audios with high accuracy, machine learning (ML) methods must be used in audio forensics. An automated gunshot audios classification method is presented in this study. To implement our automated gunshot classification method, a novel gun audios dataset was collected from YouTube with 8 classes in the first phase. A novel ML method is presented in the second phase and the proposed ML method contains three fundamental phases. These phases are a novel finger pattern (finger-pat), statistical moments and discrete wavelet transform (DWT) based feature generation network, informative/distinctive feature selection with iterative ReliefF (IRF) feature selector and classification with a k nearest neighbors (kNN) classifier (shallow) to show success of the generated and selected features by using the proposed finger-pat based feature generation network and IRF feature selector. These methods and kNN achieved 94.48% classification accuracy. These results demonstrate that our proposed method can be used in gunshot audio analysis.

Özet

Audio forensics applications and methods are very crucial to clarify crimes. To accelerate audio analysis process and classify audios with high accuracy, machine learning (ML) methods must be used in audio forensics. An automated gunshot audios classification method is presented in this study. To implement our automated gunshot classification method, a novel gun audios dataset was collected from YouTube with 8 classes in the first phase. A novel ML method is presented in the second phase and the proposed ML method contains three fundamental phases. These phases are a novel finger pattern (finger-pat), statistical moments and discrete wavelet transform (DWT) based feature generation network, informative/distinctive feature selection with iterative ReliefF (IRF) feature selector and classification with a k nearest neighbors (kNN) classifier (shallow) to show success of the generated and selected features by using the proposed finger-pat based feature generation network and IRF feature selector. These methods and kNN achieved 94.48% classification accuracy. These results demonstrate that our proposed method can be used in gunshot audio analysis.

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Cite This Article

AYDEMİR, E., DOĞAN, Ş., AKBAL, E., TUNCER, T. (2022). AN AUTOMATED GUNSHOT AUDIO CLASSIFICATION METHOD BASED ON FINGER PATTERN FEATURE GENERATOR AND ITERATIVE RELIEFF FEATURE SELECTOR. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 8 - Sayı: 14, 225–243. https://ajindex.com/articles/428567

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References (11)

  1. Casey E. Digital evidence and computer crime: Forensic science, computers, and the internet: Academic press; 2011. Turner P. Unification of digital evidence from disparate sources (digital evidence bags). Digital Investigation. 2005;2(3):223-228.
  2. Case A, Cristina A, Marziale L, Richard GG, Roussev V. FACE: Automated digital evidence discovery and correlation. digital investigation. 2008;5:S65-S75. Kenneally EE, Brown CL. Risk sensitive digital evidence collection. Digital Investigation. 2005;2(2):101-119. Turner P. Selective and intelligent imaging using digital evidence bags. digital investigation. 2006;3:59-64. Maher RC. Audio forensic examination. IEEE Signal Processing Magazine. 2009;26(2):84-94.
  3. Raponi S, Ali I, Oligeri G. Sound of Guns: Digital Forensics of Gun Audio Samples meets Artificial Intelligence. arXiv preprint arXiv:2004.07948. 2020.
  4. Begault DR, Beck SD, Maher RC. Overview of forensic audio gunshot analysis techniques. Paper presented at: Audio Engineering Society Conference: 2019 AES International Conference on Audio Forensics, 2019. Maher RC. Acoustical characterization of gunshots. Paper presented at: 2007 IEEE Workshop on Signal Processing Applications for Public Security and Forensics, 2007.
  5. Sueur J, Farina A. Ecoacoustics: the ecological investigation and interpretation of environmental sound. Biosemiotics. 2015;8(3):493-502.
  6. Xie J, Zhu M. Handcrafted features and late fusion with deep learning for bird sound classification. Ecological Informatics. 2019;52:74-81.
  7. Green M, Murphy D. Environmental sound monitoring using machine learning on mobile devices. Applied Acoustics. 2020;159:107041. Mayer P, Magno M, Benini L. Self-sustaining acoustic sensor with programmable pattern recognition for underwater monitoring. IEEE Transactions on Instrumentation and Measurement. 2019;68(7):2346-2355.
  8. Pham G-T, Baron V, Finez A, Nicolas B. High resolution source localization in underwater acoustics for deep sea mining monitoring. Paper presented at: OCEANS 2019-Marseille, 2019.
  9. Lim SJ, Jang SJ, Lim JY, Ko JH. Classification of snoring sound based on a recurrent neural network. Expert Systems With Applications. 2019;123:237-245.
  10. Patil S, Saxena A, Talreja T, Bhatti V. Medical Diagnosis of Ailments Through Supervised Learning Techniques on Sounds of the Heart and Lungs. Soft Computing and Signal Processing: Springer; 2019:253-262.
  11. Jung M, Chi S. Human activity classification based on sound recognition and residual convolutional neural network. Automati