scholarly journals Semi-fragile speech watermarking based on singular-spectrum analysis with CNN-based parameter estimation for tampering detection

2019 ◽  
Vol 8 ◽  
Author(s):  
Kasorn Galajit ◽  
Jessada Karnjana ◽  
Masashi Unoki ◽  
Pakinee Aimmanee
Keyword(s):  
Parameter Estimation ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Computational Time ◽  
Speech Signals ◽  
Hidden Information ◽  
Speech Watermarking ◽  
Singular Spectrum ◽  
Watermarking Scheme ◽  
Host Signal

AbstractA semi-fragile watermarking scheme is proposed in this paper for detecting tampering in speech signals. The scheme can effectively identify whether or not original signals have been tampered with by embedding hidden information into them. It is based on singular-spectrum analysis, where watermark bits are embedded into speech signals by modifying a part of the singular spectrum of a host signal. Convolutional neural network (CNN)-based parameter estimation is deployed to quickly and properly select the part of the singular spectrum to be modified so that it meets inaudibility and robustness requirements. Evaluation results show that CNN-based parameter estimation reduces the computational time of the scheme and also makes the scheme blind, i.e. we require only a watermarked signal in order to extract a hidden watermark. In addition, a semi-fragility property, which allows us to detect tampering in speech signals, is achieved. Moreover, due to the time efficiency of the CNN-based parameter estimation, the proposed scheme can be practically used in real-time applications.

Filtering and parameter estimation of surface-NMR data using singular spectrum analysis

2016 ◽  
Vol 130 ◽  
pp. 118-130 ◽  
Author(s):  
Reza Ghanati ◽  
Mohammad Kazem Hafizi ◽  
Rahim Mahmoudvand ◽  
Mahdi Fallahsafari
Keyword(s):  
Parameter Estimation ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Singular Spectrum ◽  
Nmr Data

scholarly journals The Decomposition and Forecasting of Mutual Investment Funds Using Singular Spectrum Analysis

Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 83 ◽  
Author(s):  
Paulo Canas Rodrigues ◽  
Jonatha Pimentel ◽  
Patrick Messala ◽  
Mohammad Kazemi
Keyword(s):  
Time Series ◽  
Spectrum Analysis ◽  
Time Series Data ◽  
Singular Spectrum Analysis ◽  
Model Fit ◽  
Series Data ◽  
Computational Time ◽  
Investment Funds ◽  
Singular Spectrum ◽  
Non Parametric

Singular spectrum analysis (SSA) is a non-parametric method that breaks down a time series into a set of components that can be interpreted and grouped as trend, periodicity, and noise, emphasizing the separability of the underlying components and separate periodicities that occur at different time scales. The original time series can be recovered by summing all components. However, only the components associated to the signal should be considered for the reconstruction of the noise-free time series and to conduct forecasts. When the time series data has the presence of outliers, SSA and other classic parametric and non-parametric methods might result in misleading conclusions and robust methodologies should be used. In this paper we consider the use of two robust SSA algorithms for model fit and one for model forecasting. The classic SSA model, the robust SSA alternatives, and the autoregressive integrated moving average (ARIMA) model are compared in terms of computational time and accuracy for model fit and model forecast, using a simulation example and time series data from the quotas and returns of six mutual investment funds. When outliers are present in the data, the simulation study shows that the robust SSA algorithms outperform the classical ARIMA and SSA models.

scholarly journals Audio Watermarking Scheme Based on Singular Spectrum Analysis and Psychoacoustic Model with Self-Synchronization

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Jessada Karnjana ◽  
Masashi Unoki ◽  
Pakinee Aimmanee ◽  
Chai Wutiwiwatchai
Keyword(s):  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Extraction Process ◽  
Audio Watermarking ◽  
Psychoacoustic Model ◽  
Singular Spectrum ◽  
Convex Part ◽  
Spectrum Curve ◽  
Watermarking Scheme ◽  
Host Signals

This paper proposes a blind, inaudible, and robust audio watermarking scheme based on singular spectrum analysis (SSA) and the psychoacoustic model 1 (ISO/IEC 11172-3). In this work, SSA is used to analyze the host signals and to extract the singular spectra. A watermark is embedded into the host signals by modifying the singular spectra which are in the convex part of the singular spectrum curve so that this part becomes concave. This modification certainly affects the inaudibility and robustness properties of the watermarking scheme. To satisfy both properties, the modified part of the singular spectrum is determined by a novel parameter selection method based on the psychoacoustic model. The test results showed that the proposed scheme achieves not only inaudibility and robustness but also blindness. In addition, this work showed that the extraction process of a variant of the proposed scheme can extract the watermark without assuming to know the frame positions in advance and without embedding additional synchronization code into the audio content.

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