Deep convolutional neural networks for double compressed AMR audio detection

Abstract Detection of double compressed (DC) adaptive multi‐rate (AMR) audio recordings is a challenging audio forensic problem and has received great attention in recent years. Here, the authors propose to use convolutional neural networks (CNN) for DC AMR audio detection. The CNN is used as (i) an...

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Main Authors: Aykut Büker, Cemal Hanilçi
Format: Article
Language:English
Published: Wiley 2021-06-01
Series:IET Signal Processing
Subjects:
Online Access:https://doi.org/10.1049/sil2.12028
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author Aykut Büker
Cemal Hanilçi
author_facet Aykut Büker
Cemal Hanilçi
author_sort Aykut Büker
collection DOAJ
description Abstract Detection of double compressed (DC) adaptive multi‐rate (AMR) audio recordings is a challenging audio forensic problem and has received great attention in recent years. Here, the authors propose to use convolutional neural networks (CNN) for DC AMR audio detection. The CNN is used as (i) an end‐to‐end DC AMR audio detection system and (ii) a feature extractor. The end‐to‐end system receives the audio spectrogram as the input and returns the decision whether the input audio is single compressed (SC) or DC. As a feature extractor in turn, it is used to extract discriminative features and then these features are modelled using support vector machines (SVM) classifier. Our extensive analysis conducted on four different datasets shows the success of the proposed system and provides new findings related to the problem. Firstly, double compression has a considerable impact on the high frequency components of the signal. Secondly, the proposed system yields great performance independent of the recording device or environment. Thirdly, when previously altered files are used in the experiments, 97.41% detection rate is obtained with the CNN system. Finally, the cross‐dataset evaluation experiments show that the proposed system is very effective in case of a mismatch between training and test datasets.
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spelling doaj-art-5f66e389d28848929463f6db2707c3a52025-02-03T06:47:26ZengWileyIET Signal Processing1751-96751751-96832021-06-0115426528010.1049/sil2.12028Deep convolutional neural networks for double compressed AMR audio detectionAykut Büker0Cemal Hanilçi1Department of Electrical and Electronics Engineering Bursa Technical University Bursa TurkeyDepartment of Electrical and Electronics Engineering Bursa Technical University Bursa TurkeyAbstract Detection of double compressed (DC) adaptive multi‐rate (AMR) audio recordings is a challenging audio forensic problem and has received great attention in recent years. Here, the authors propose to use convolutional neural networks (CNN) for DC AMR audio detection. The CNN is used as (i) an end‐to‐end DC AMR audio detection system and (ii) a feature extractor. The end‐to‐end system receives the audio spectrogram as the input and returns the decision whether the input audio is single compressed (SC) or DC. As a feature extractor in turn, it is used to extract discriminative features and then these features are modelled using support vector machines (SVM) classifier. Our extensive analysis conducted on four different datasets shows the success of the proposed system and provides new findings related to the problem. Firstly, double compression has a considerable impact on the high frequency components of the signal. Secondly, the proposed system yields great performance independent of the recording device or environment. Thirdly, when previously altered files are used in the experiments, 97.41% detection rate is obtained with the CNN system. Finally, the cross‐dataset evaluation experiments show that the proposed system is very effective in case of a mismatch between training and test datasets.https://doi.org/10.1049/sil2.12028audio codingaudio recordingaudio signal processingdata compressionfeature extractionsignal classification
spellingShingle Aykut Büker
Cemal Hanilçi
Deep convolutional neural networks for double compressed AMR audio detection
IET Signal Processing
audio coding
audio recording
audio signal processing
data compression
feature extraction
signal classification
title Deep convolutional neural networks for double compressed AMR audio detection
title_full Deep convolutional neural networks for double compressed AMR audio detection
title_fullStr Deep convolutional neural networks for double compressed AMR audio detection
title_full_unstemmed Deep convolutional neural networks for double compressed AMR audio detection
title_short Deep convolutional neural networks for double compressed AMR audio detection
title_sort deep convolutional neural networks for double compressed amr audio detection
topic audio coding
audio recording
audio signal processing
data compression
feature extraction
signal classification
url https://doi.org/10.1049/sil2.12028
work_keys_str_mv AT aykutbuker deepconvolutionalneuralnetworksfordoublecompressedamraudiodetection
AT cemalhanilci deepconvolutionalneuralnetworksfordoublecompressedamraudiodetection