scholarly journals Time-frequency analysis of spread spectrum based communication and audio watermarking systems

2021 ◽  
Author(s):  
Serhat Erküçük
Keyword(s):  
Frequency Analysis ◽  
Spread Spectrum ◽  
Communication Systems ◽  
Estimation Algorithm ◽  
Audio Watermarking ◽  
Spread Spectrum Communication ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Symbol Detection ◽  
Stationary Signals

In this study, we present novel applications of time-frequency analysis to spread spectrum based communication and audio watermarking systems. Our objective is to detect and estimate non-stationary signals, such as chirps, that are characterized by directional elements in the time-frequency plane. Towards this goal, we model non-stationary signals using the matching pursuit decomposition algorithm, generate a positive time-frequency representation of the signal model using the Wigner-Ville distribution and estimate the energy varying directional elements using a line detection algorithm based on the Hough-Radon transform. Spread spectrum communication systems frequently encounter nonstationary signals with energy varying directional elements as hostile jamming signals. In this thesis, we develop a new interference excision algorithm for spread spectrum communication systems based on the directional element estimation algorithm. At the receiver, we first excise the interference from the spread spectrum signal before despreading and data symbol detection. The new algorithm can excise single and multicomponent interferences such that the spread spectrum system can reliably detect the transmitted message symbols even, when the interference power exceeds the jamming margin of the system. We verify the effectiveness of the interference excision algorithm using simulation studies. Watermarking is the process of embedding imperceptible data into the host signal for marking the copyright ownership. The embedded data should be extractable to prove ownership. Watermarking systems face problems similar to those in spread spectrum communication systems, namely, intentional attacks by the adversaries. In watermarking, the adversaries try to obliterate the embedded watermark in order to prevent its detection by authorized parties. In this thesis, we develop a spread spectrum audio watermarking scheme, where we embed perceptually shaped linear chirps as watermark messages. The directional elements of the chirp signals represent different watermark messages. We extract the watermark by first detecting the transmitted message symbols in the spread spectrum signal. We then use the directional element estimation algorithm based on the time-frequency analysis as a post-processing tool to minimize the effects of hostile attacks on the extractability of the embedded watermark. We demonstrate the robustness of the algorithm by extracting the watermark correctly after common signal processing operations representing hostile attacks by adversaries.

scholarly journals Time-frequency analysis of spread spectrum based communication and audio watermarking systems

2021 ◽  
Author(s):  
Serhat Erküçük
Keyword(s):  
Frequency Analysis ◽  
Spread Spectrum ◽  
Communication Systems ◽  
Estimation Algorithm ◽  
Audio Watermarking ◽  
Spread Spectrum Communication ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Symbol Detection ◽  
Stationary Signals

In this study, we present novel applications of time-frequency analysis to spread spectrum based communication and audio watermarking systems. Our objective is to detect and estimate non-stationary signals, such as chirps, that are characterized by directional elements in the time-frequency plane. Towards this goal, we model non-stationary signals using the matching pursuit decomposition algorithm, generate a positive time-frequency representation of the signal model using the Wigner-Ville distribution and estimate the energy varying directional elements using a line detection algorithm based on the Hough-Radon transform. Spread spectrum communication systems frequently encounter nonstationary signals with energy varying directional elements as hostile jamming signals. In this thesis, we develop a new interference excision algorithm for spread spectrum communication systems based on the directional element estimation algorithm. At the receiver, we first excise the interference from the spread spectrum signal before despreading and data symbol detection. The new algorithm can excise single and multicomponent interferences such that the spread spectrum system can reliably detect the transmitted message symbols even, when the interference power exceeds the jamming margin of the system. We verify the effectiveness of the interference excision algorithm using simulation studies. Watermarking is the process of embedding imperceptible data into the host signal for marking the copyright ownership. The embedded data should be extractable to prove ownership. Watermarking systems face problems similar to those in spread spectrum communication systems, namely, intentional attacks by the adversaries. In watermarking, the adversaries try to obliterate the embedded watermark in order to prevent its detection by authorized parties. In this thesis, we develop a spread spectrum audio watermarking scheme, where we embed perceptually shaped linear chirps as watermark messages. The directional elements of the chirp signals represent different watermark messages. We extract the watermark by first detecting the transmitted message symbols in the spread spectrum signal. We then use the directional element estimation algorithm based on the time-frequency analysis as a post-processing tool to minimize the effects of hostile attacks on the extractability of the embedded watermark. We demonstrate the robustness of the algorithm by extracting the watermark correctly after common signal processing operations representing hostile attacks by adversaries.

scholarly journals Content Based Audio Watermarking and Retrieval Using Time-Frequency Analysis

2021 ◽  
Author(s):  
Shahrzad Esmaili
Keyword(s):  
Frequency Analysis ◽  
Spread Spectrum ◽  
Data Privacy ◽  
Audio Signal ◽  
Maximum Energy ◽  
Audio Watermarking ◽  
Audio Signals ◽  
Time Frequency Analysis ◽  
Linear Discriminant ◽  
Time Frequency

This research focuses on the application of joint time-frequency (TF) analysis for watermarking and classifying different audio signals. Time frequency analysis which originated in the 1930s has often been used to model the non-stationary behaviour of speech and audio signals. By taking into consideration the human auditory system which has many non-linear effects and its masking properties, we can extract efficient features from the TF domain to watermark or classify signals. This novel audio watermarking scheme is based on spread spectrum techniques and uses content-based analysis to detect the instananeous mean frequency (IMF) of the input signal. The watermark is embedded in this perceptually significant region such that it will resist attacks. Audio watermarking offers a solution to data privacy and helps to protect the rights of the artists and copyright holders. Using the IMF, we aim to keep the watermark imperceptible while maximizing its robustness. In this case, 25 bits are embedded and recovered witin a 5 s sample of an audio signal. This scheme has shown to be robust against various signal processing attacks including filtering, MP3 compression, additive moise and resampling with a bit error rate in the range of 0-13%. In addition content-based classification is performed using TF analysis to classify sounds into 6 music groups consisting of rock, classical, folk, jazz and pop. The features that are extracted include entropy, centroid, centroid ratio, bandwidth, silence ratio, energy ratio, frequency location of minimum and maximum energy. Using a database of 143 signals, a set of 10 time-frequncy features are extracted and an accuracy of classification of around 93.0% using regular linear discriminant analysis or 92.3% using leave one out method is achieved.

scholarly journals Content Based Audio Watermarking and Retrieval Using Time-Frequency Analysis

2021 ◽  
Author(s):  
Shahrzad Esmaili
Keyword(s):  
Frequency Analysis ◽  
Spread Spectrum ◽  
Data Privacy ◽  
Audio Signal ◽  
Maximum Energy ◽  
Audio Watermarking ◽  
Audio Signals ◽  
Time Frequency Analysis ◽  
Linear Discriminant ◽  
Time Frequency

This research focuses on the application of joint time-frequency (TF) analysis for watermarking and classifying different audio signals. Time frequency analysis which originated in the 1930s has often been used to model the non-stationary behaviour of speech and audio signals. By taking into consideration the human auditory system which has many non-linear effects and its masking properties, we can extract efficient features from the TF domain to watermark or classify signals. This novel audio watermarking scheme is based on spread spectrum techniques and uses content-based analysis to detect the instananeous mean frequency (IMF) of the input signal. The watermark is embedded in this perceptually significant region such that it will resist attacks. Audio watermarking offers a solution to data privacy and helps to protect the rights of the artists and copyright holders. Using the IMF, we aim to keep the watermark imperceptible while maximizing its robustness. In this case, 25 bits are embedded and recovered witin a 5 s sample of an audio signal. This scheme has shown to be robust against various signal processing attacks including filtering, MP3 compression, additive moise and resampling with a bit error rate in the range of 0-13%. In addition content-based classification is performed using TF analysis to classify sounds into 6 music groups consisting of rock, classical, folk, jazz and pop. The features that are extracted include entropy, centroid, centroid ratio, bandwidth, silence ratio, energy ratio, frequency location of minimum and maximum energy. Using a database of 143 signals, a set of 10 time-frequncy features are extracted and an accuracy of classification of around 93.0% using regular linear discriminant analysis or 92.3% using leave one out method is achieved.

scholarly journals Time–Frequency-Analysis-Based Blind Modulation Classification for Multiple-Antenna Systems

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 231
Author(s):  
Weiheng Jiang ◽  
Xiaogang Wu ◽  
Yimou Wang ◽  
Bolin Chen ◽  
Wenjiang Feng ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Classification Accuracy ◽  
Communication Systems ◽  
Mimo Systems ◽  
Decision Fusion ◽  
Frequency Characteristics ◽  
Modulation Classification ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Average Classification Accuracy

Blind modulation classification is an important step in implementing cognitive radio networks. The multiple-input multiple-output (MIMO) technique is widely used in military and civil communication systems. Due to the lack of prior information about channel parameters and the overlapping of signals in MIMO systems, the traditional likelihood-based and feature-based approaches cannot be applied in these scenarios directly. Hence, in this paper, to resolve the problem of blind modulation classification in MIMO systems, the time–frequency analysis method based on the windowed short-time Fourier transform was used to analyze the time–frequency characteristics of time-domain modulated signals. Then, the extracted time–frequency characteristics are converted into red–green–blue (RGB) spectrogram images, and the convolutional neural network based on transfer learning was applied to classify the modulation types according to the RGB spectrogram images. Finally, a decision fusion module was used to fuse the classification results of all the receiving antennas. Through simulations, we analyzed the classification performance at different signal-to-noise ratios (SNRs); the results indicate that, for the single-input single-output (SISO) network, our proposed scheme can achieve 92.37% and 99.12% average classification accuracy at SNRs of −4 and 10 dB, respectively. For the MIMO network, our scheme achieves 80.42% and 87.92% average classification accuracy at −4 and 10 dB, respectively. The proposed method greatly improves the accuracy of modulation classification in MIMO networks.

Separation of Intercepted Multi-Radar Signals Based on Parameterized Time-Frequency Analysis

Frequenz ◽  
2016 ◽  
Vol 70 (9-10) ◽  
Author(s):  
W. L. Lu ◽  
J. W. Xie ◽  
H. M. Wang ◽  
C. Sheng
Keyword(s):  
Frequency Analysis ◽  
Simulated Data ◽  
Radar Signal ◽  
Electronic Warfare ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Frequency Domains ◽  
Stationary Signals ◽  
Radar Signals ◽  
Electronic Intelligence

AbstractModern radars use complex waveforms to obtain high detection performance and low probabilities of interception and identification. Signals intercepted from multiple radars overlap considerably in both the time and frequency domains and are difficult to separate with primary time parameters. Time–frequency analysis (TFA), as a key signal-processing tool, can provide better insight into the signal than conventional methods. In particular, among the various types of TFA, parameterized time-frequency analysis (PTFA) has shown great potential to investigate the time–frequency features of such non-stationary signals. In this paper, we propose a procedure for PTFA to separate overlapped radar signals; it includes five steps: initiation, parameterized time-frequency analysis, demodulating the signal of interest, adaptive filtering and recovering the signal. The effectiveness of the method was verified with simulated data and an intercepted radar signal received in a microwave laboratory. The results show that the proposed method has good performance and has potential in electronic reconnaissance applications, such as electronic intelligence, electronic warfare support measures, and radar warning.

An Investigation of Feature Extraction of Local Energy in Joint Time-Frequency Analysis Based on Process Systems Modeling and Simulation

2012 ◽  
Vol 198-199 ◽  
pp. 803-807
Author(s):  
Feng Li Wang ◽  
Shu Lin Duan ◽  
Hong Tao Gao
Keyword(s):  
Feature Extraction ◽  
Frequency Analysis ◽  
Local Energy ◽  
Simulation Research ◽  
Time Frequency Analysis ◽  
Process Systems ◽  
Frequency Space ◽  
Time Frequency ◽  
Stationary Signals ◽  
Local Wave

Aiming at the characteristics of local properties of the non-stationary signals, a noval feature extraction approach based on the local energy in joint time-frequency analysis is proposed. The concept of local energy in joint time- frequency analysis based on local wave analysis was used to measure the signal energy in time-frequency space of the signal. Firstly, analyze the signal with local wave method and then make Hilbert transformation of it. Then partition several areas in time frequency space and compute its local energy. From the expression of local wave time-frequency distributing, not only total energy of signal can be computed but also local energy in time-frequency space. Simulation research indicates that the developed approach was effective.

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