scholarly journals A Real-time Implementation for the Speech Steganography using Short-Time Fourier Transformior Secured Mobile Communication

2021 ◽  
Vol 2089 (1) ◽  
pp. 012066
Author(s):  
Rajeev Shrivastava ◽  
Mangal Singh ◽  
RakhiThakur ◽  
Kalluri Saidatta Subrahmanya Ravi Teja
Keyword(s):  
Information Hiding ◽  
Deep Neural Networks ◽  
Single Carrier ◽  
Multiple Datasets ◽  
Speech Steganography ◽  
Channel Distortion ◽  
Voice Data ◽  
Short Time ◽  
Implementation Technique ◽  
Message Signal

Abstract Steganography can be described as approach of masking an undisclosed message with a normal message which is known as the Carrier message signal. DSP techniques, such as LSB encoding, have historically been implemented for secret information hiding. Utilization ofsteganography functions of deep neural networks for voice data is something this paper will present. This paper also demonstrate that the steganography techniques suggested for vision are less suitable for speech signals this paper present a implementation technique that involves the use of ISTFT and STFT as differentiablelayers in the network. Empirically, the efficacy of the proposed methods based on multiple datasets of speech should be demonstrated and the outcome are examined quantitatively and qualitatively. Using of multiple decoders or a single conditional decoder helps to hide multiple signals in a single carrier signal. Finally, under various channel distortion situations, this model Qualitative studies indicate that human listeners cannot detect changes made to the carrier and hence the decoded messages are highly intelligible.

scholarly journals Evaluation of Deep Learning-Based Neural Network Methods for Cloud Detection and Segmentation

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6156
Author(s):  
Stefan Hensel ◽  
Marin B. Marinov ◽  
Michael Koch ◽  
Dimitar Arnaudov
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Data Sets ◽  
Cloud Detection ◽  
Camera System ◽  
Data Set ◽  
Network Methods ◽  
Segmentation Approach ◽  
Coverage Prediction ◽  
Short Time

This paper presents a systematic approach for accurate short-time cloud coverage prediction based on a machine learning (ML) approach. Based on a newly built omnidirectional ground-based sky camera system, local training and evaluation data sets were created. These were used to train several state-of-the-art deep neural networks for object detection and segmentation. For this purpose, the camera-generated a full hemispherical image every 30 min over two months in daylight conditions with a fish-eye lens. From this data set, a subset of images was selected for training and evaluation according to various criteria. Deep neural networks, based on the two-stage R-CNN architecture, were trained and compared with a U-net segmentation approach implemented by CloudSegNet. All chosen deep networks were then evaluated and compared according to the local situation.

scholarly journals Interaffection of Multiple Datasets with Neural Networks in Speech Emotion Recognition

2020 ◽  
Author(s):  
Ronnypetson Da Silva ◽  
Valter M. Filho ◽  
Mario Souza
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Emotion Recognition ◽  
Deep Neural Networks ◽  
Speech Emotion Recognition ◽  
Network Architectures ◽  
Shared Representations ◽  
Multiple Datasets ◽  
Neural Network Architectures

Many works that apply Deep Neural Networks (DNNs) to Speech Emotion Recognition (SER) use single datasets or train and evaluate the models separately when using multiple datasets. Those datasets are constructed with specific guidelines and the subjective nature of the labels for SER makes it difficult to obtain robust and general models. We investigate how DNNs learn shared representations for different datasets in both multi-task and unified setups. We also analyse how each dataset benefits from others in different combinations of datasets and popular neural network architectures. We show that the longstanding belief of more data resulting in more general models doesn’t always hold for SER, as different dataset and meta-parameter combinations hold the best result for each of the analysed datasets.

scholarly journals Semantic Explanation for Deep Neural Networks Using Feature Interactions

2021 ◽  
Vol 17 (3s) ◽  
pp. 1-19
Author(s):  
Bohui Xia ◽  
Xueting Wang ◽  
Toshihiko Yamasaki
Keyword(s):  
Deep Neural Networks ◽  
Prediction Method ◽  
Target Variable ◽  
Practical Applications ◽  
Feature Interactions ◽  
Multiple Datasets ◽  
Learning Techniques ◽  
Additive Formulation ◽  
Factorization Machine ◽  
Detailed Interpretation

Given the promising results obtained by deep-learning techniques in multimedia analysis, the explainability of predictions made by networks has become important in practical applications. We present a method to generate semantic and quantitative explanations that are easily interpretable by humans. The previous work to obtain such explanations has focused on the contributions of each feature, taking their sum to be the prediction result for a target variable; the lack of discriminative power due to this simple additive formulation led to low explanatory performance. Our method considers not only individual features but also their interactions, for a more detailed interpretation of the decisions made by networks. The algorithm is based on the factorization machine, a prediction method that calculates factor vectors for each feature. We conducted experiments on multiple datasets with different models to validate our method, achieving higher performance than the previous work. We show that including interactions not only generates explanations but also makes them richer and is able to convey more information. We show examples of produced explanations in a simple visual format and verify that they are easily interpretable and plausible.

scholarly journals Hide and Speak: Towards Deep Neural Networks for Speech Steganography

2020 ◽  
Author(s):  
Felix Kreuk ◽  
Yossi Adi ◽  
Bhiksha Raj ◽  
Rita Singh ◽  
Joseph Keshet
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Speech Steganography

scholarly journals Guided Dropout

2019 ◽  
Vol 33 ◽  
pp. 4065-4072 ◽  
Author(s):  
Rohit Keshari ◽  
Richa Singh ◽  
Mayank Vatsa
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Experimental Evaluation ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Multiple Datasets ◽  
Selection Of

Dropout is often used in deep neural networks to prevent over-fitting. Conventionally, dropout training invokes random drop of nodes from the hidden layers of a Neural Network. It is our hypothesis that a guided selection of nodes for intelligent dropout can lead to better generalization as compared to the traditional dropout. In this research, we propose “guided dropout” for training deep neural network which drop nodes by measuring the strength of each node. We also demonstrate that conventional dropout is a specific case of the proposed guided dropout. Experimental evaluation on multiple datasets including MNIST, CIFAR10, CIFAR100, SVHN, and Tiny ImageNet demonstrate the efficacy of the proposed guided dropout.

scholarly journals An Advanced CNN-LSTM Model for Cryptocurrency Forecasting

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 287
Author(s):  
Ioannis E. Livieris ◽  
Niki Kiriakidou ◽  
Stavros Stavroyiannis ◽  
Panagiotis Pintelas
Keyword(s):  
Portfolio Management ◽  
Deep Neural Networks ◽  
Computational Cost ◽  
Forecasting Model ◽  
Time Period ◽  
Proposed Model ◽  
Multiple Input ◽  
Short Time ◽  
Fully Connected ◽  
Financial Transactions

Nowadays, cryptocurrencies are established and widely recognized as an alternative exchange currency method. They have infiltrated most financial transactions and as a result cryptocurrency trade is generally considered one of the most popular and promising types of profitable investments. Nevertheless, this constantly increasing financial market is characterized by significant volatility and strong price fluctuations over a short-time period therefore, the development of an accurate and reliable forecasting model is considered essential for portfolio management and optimization. In this research, we propose a multiple-input deep neural network model for the prediction of cryptocurrency price and movement. The proposed forecasting model utilizes as inputs different cryptocurrency data and handles them independently in order to exploit useful information from each cryptocurrency separately. An extensive empirical study was performed using three consecutive years of cryptocurrency data from three cryptocurrencies with the highest market capitalization i.e., Bitcoin (BTC), Etherium (ETH), and Ripple (XRP). The detailed experimental analysis revealed that the proposed model has the ability to efficiently exploit mixed cryptocurrency data, reduces overfitting and decreases the computational cost in comparison with traditional fully-connected deep neural networks.

What the replication reformation wrought

2018 ◽  
Vol 41 ◽  
Author(s):  
Barbara A. Spellman ◽  
Daniel Kahneman
Keyword(s):  
Reform Movement ◽  
Short Time

AbstractReplication failures were among the triggers of a reform movement which, in a very short time, has been enormously useful in raising standards and improving methods. As a result, the massive multilab multi-experiment replication projects have served their purpose and will die out. We describe other types of replications – both friendly and adversarial – that should continue to be beneficial.

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