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Detecting double compressed AMR audio using deep learning
2014
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Usually, the tampered AMR audio is double compressed AMR audio. In this paper, we proposed a method to detect the double compressed AMR audio. ...
The experimental results show that our method is effective to detect the double compressed AMR audio. Besides, the potential application of this technique is also discussed. ...
A stacked autoencoder is a neural network consisting of multiple layers of autoencoders in which the outputs of each layer is wired to the inputs of the successive layer. ...
doi:10.1109/icassp.2014.6854084
dblp:conf/icassp/LuoYH14
fatcat:p4p6dyqhovcxlnm7fiejnn3tiq
An Antiforensic Method against AMR Compression Detection
2020
Security and Communication Networks
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The GAN framework is utilized to modify double AMR compressed audio to have the underlying statistics of single compressed one. ...
The experimental results demonstrate that the proposed method is capable of removing the forensically detectable artifacts of AMR compression under various ratios with an average successful attack rate ...
Wong Magna Fund of Ningbo University. ...
doi:10.1155/2020/8849902
fatcat:sklekuaaufcxxj64qmebxzzoua
AMR Compressed-Domain Analysis for Multimedia Forensics Double Compression Detection
2019
Revista Brasileira de Ciências Policiais
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By means of feature statistical analysis, it is possible to show that they can be used to achieve AMR double compression detection in an effective way. ...
The published works in literature about double compression detection are based on decoded waveform of the AMR files to extract features. ...
More recently, a stacked autoencoder--based method achieved better accuracies about 98% for AMR double compression detection, once again using raw decompressed AMR as the input. ...
doi:10.31412/rbcp.v9i2.534
fatcat:nsrbiht5vrgofci6gxayxr42qi
Deep convolutional neural networks for double compressed AMR audio detection
2021
IET Signal Processing
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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. ...
hidden layers, stacked autoencoder (SAE) and MLP with dropout layers [22] . ...
doi:10.1049/sil2.12028
fatcat:a4wbfwtm5vgwphjax5jrye777u
Deep Learning in Information Security
[article]
2018
arXiv
pre-print
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Deep Learning is a sub-field of machine learning, which uses models that are composed of multiple layers. ...
Consequently, representations that are used to solve a task are learned from the data instead of being manually designed. ...
Detection of double compressed AMR audio using stacked
autoencoder. IEEE Transactions on Information Forensics and Security, 12(2):432–444, 2017. ISSN 15566013. ...
arXiv:1809.04332v1
fatcat:xfb7lgrkw5cirdl3qvmg3ssnbi
Convolutional Neural Networks to Enhance Coded Speech
2019
IEEE/ACM Transactions on Audio Speech and Language Processing
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His research interests include speech enhancement, improved speech and audio decoding, and deep learning approaches. ...
The time-domain approach follows an end-to-end fashion, whereas the cepstral domain approach uses analysis-synthesis with cepstral domain features. ...
Elshamy for providing an implementation of both DCT-II and the IDCT-II and J. Abel for advice concerning the setup of the subjective listening test. ...
doi:10.1109/taslp.2018.2887337
fatcat:qiqne5kuvbfcfbvkxmwoy7jya4
Table of contents
2021
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Luo, Graduate School of Information, Production and Systems, Waseda University, Japan IVMSP-12.3: UNSUPERVISED STACKED CAPSULE AUTOENCODER FOR .................................................. 1825 HYPERSPECTRAL ...
ON .......................................... 2525 MICROSTRUCTURE ORIENTATION ESTIMATION Yuhao Sun, Xin Liao, Hunan University, China; Jianfeng Liu, China Jiliang University, China IFS-2.3: LEARNING DOUBLE-COMPRESSION ...
doi:10.1109/icassp39728.2021.9414617
fatcat:m5ugnnuk7nacbd6jr6gv2lsfby
Convolutional Neural Networks to Enhance Coded Speech
[article]
2019
arXiv
pre-print
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The time domain approach follows an end-to-end fashion, while the cepstral domain approach uses analysis-synthesis with cepstral domain features. ...
The proposed postprocessor improves speech quality (PESQ) by up to 0.25 MOS-LQO points for G.711, 0.30 points for G.726, 0.82 points for G.722, and 0.26 points for adaptive multirate wideband codec (AMR-WB ...
Elshamy for providing an implementation of both DCT-II and the IDCT-II and J. Abel for advice concerning the setup of the subjective listening test. ...
arXiv:1806.09411v4
fatcat:ixkasnh7nbfavccgighfkf7ife
Interpolation Consistency Training for Semi-supervised Learning
2019
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
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ICT encourages the prediction at an interpolation of unlabeled points to be consistent with the interpolation of the predictions at those points. ...
In classification problems, ICT moves the decision boundary to low-density regions of the data distribution. ...
In section 2.2, I briefly describe the working principle of the Auto-Encoders, which is followed by the motivation and summary of Adversarial Mixup Resynthesizer (Publication II). ...
doi:10.24963/ijcai.2019/504
dblp:conf/ijcai/VermaLKBL19
fatcat:bucjagfaybei5boup7b56yqngu
Graph Neural Networks in IoT: A Survey
[article]
2022
arXiv
pre-print
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In this survey, we present a comprehensive review of recent advances in the application of GNNs to the IoT field, including a deep dive analysis of GNN design in various IoT sensing environments, an overarching ...
The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily lives: healthcare, home, transportation, manufacturing, supply chain, and so on. ...
In the scenarios of using GNNs for anomaly detection, Tang et al. ...
arXiv:2203.15935v2
fatcat:jkqg5ukg5fezbewu5mr5hqsp4e
Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation – A Comprehensive Review
[article]
2023
arXiv
pre-print
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This review paper presents a comprehensive overview of end-to-end deep learning frameworks used in the context of autonomous navigation, including obstacle detection, scene perception, path planning, and ...
The review highlights the rapid growth of deep learning in engineering data science and its development of innovative navigation methods. ...
We are deeply appreciative of their contributions and the time they dedicated to helping us. ...
arXiv:2302.11089v2
fatcat:axsn4a5lb5fcvikj5qqlnkxkv4
2021 Index IEEE Transactions on Instrumentation and Measurement Vol. 70
2021
IEEE Transactions on Instrumentation and Measurement
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The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, and article number. ...
., +, TIM 2021 2510610 Deep Convolutional Stack Autoencoder of Process Adaptive VMD Data With Robust Multikernel RVFLN for Power Quality Events Recognition. ...
., +, TIM 2021 2510610 Deep Convolutional Stack Autoencoder of Process Adaptive VMD Data With Robust Multikernel RVFLN for Power Quality Events Recognition. ...
doi:10.1109/tim.2022.3156705
fatcat:dmqderzenrcopoyipv3v4vh4ry
Deep Neural Networks for End-to-End Optimized Speech Coding
2017
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Modern compression algorithms are the result of years of research; industry standards such as MP3, JPEG, and G.722.1 required complex hand-engineered compression pipelines, often with much manual tuning ...
Our aim is to extend these "deep learning" methods into the domain of compression. ...
compressive because it performs the task of compression, and a residual autoencoder because its structure is derived from residual neural networks [20] and autoencoders [48] . ...
doi:10.13016/m2m03xx8h
fatcat:fcwbxqe5b5abhh45xl34scbgny
Program
2020
2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)
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The proposed architecture mainly uses the LSTM layers in a stacked fashion with a different number of units in each layer. ...
for Smart Data Compression Under NOMA
Uplink Protocol Mohamed Elsayed (Qatar University, Qatar); Ahmed Badawy (Politecnico di Torino, Italy); Ahmed El Shafie (Qualcomm Inc., USA); Amr Mohamed and Tamer ...
Variational Autoencoders (CVAEs) for all classes separately and have evaluated the models using the inception score. ...
doi:10.1109/ccece47787.2020.9255763
fatcat:mpf7smikpfc77bu73ciqstdagm
A Roadmap for Big Model
[article]
2022
arXiv
pre-print
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2022 pre-print arXiv:2203.14101v3 fatcat:m7pyxdvamfdndaipb3lew3syhyOther Versions
At the end of this paper, we conclude the further development of BMs in a more general view. ...
At present, there is a lack of research work that sorts out the overall progress of BMs and guides the follow-up research. ...
The model is tested on various tasks with structured inputs and outputs including multi-task language understanding, optical flow prediction, video+audio autoencoding, etc. ...
arXiv:2203.14101v4
fatcat:rdikzudoezak5b36cf6hhne5u4
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