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Dergiler / 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
Sayfa
225–243
Yayın Tarihi
2022-10-12
DOI
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Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Audio forensics applications and methods are very crucial to clarify crimes.
📊
Temel Bulgular & Veri:To accelerate audio analysis process and classify audios with high accuracy, machine learning (ML) methods must be used in audio forensics.
💡
Sonuç & Katkı:These results demonstrate that our proposed method can be used in gunshot audio analysis.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Gunshot audio classification · finger pattern · iterative ReliefF

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.

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.

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Bu Makaleye Atıf Yap

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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Kaynakça (11)

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