# Time-Frequency Analysis and Its Application in Digital Watermarking

- Srdjan Stanković
^{1}Email author

**2010**:579295

https://doi.org/10.1155/2010/579295

© Srdjan Stanković. 2010

**Received: **14 February 2010

**Accepted: **7 June 2010

**Published: **1 July 2010

## Abstract

A review of time-frequency analysis and some aspects of its applications in digital watermarking are presented. The main advantages and drawbacks of various time-frequency distributions are first discussed. The aim of this theoretical overview is to facilitate an appropriate distribution selection in a specific application. Different aspects of the time-frequency analysis when applied to digital watermarking are then presented. In particular, the method that maps time-frequency characteristics of a host signal to the pseudo noise watermark sequence is thoroughly discussed. This approach is presented in the multidimensional form and then applied to digital audio, digital image, and digital video watermarking. Finally, the theoretical considerations are illustrated by various numerical and real-life examples.

## 1. Introduction

Theoretical aspects of time-frequency analysis have been intensively studied over the last two decades [1–27]. In parallel, their various applications have been exploited as well. Namely, for an efficient analysis of nonstationary signals, such are radar, sonar, biomedical, seismic, and multimedia signals, time-frequency representations are required. Time-frequency distributions are most commonly used for this purpose. Many of the researchers have made significant efforts in defining a distribution that is optimal for a wide class of frequency-modulated signals [8–11]. As a result, a number of time-frequency distributions have been proposed. However, the efficiency of each of them is more or less limited to a specific class of signals and, consequently, to a specific application. One of the goals of this paper is to highlight the most important features of some popular time-frequency distributions and to give an idea of how to choose the most appropriate distribution depending on the signal form. The linear, quadratic, higher-order, and multiwindow time-frequency distributions are considered. The short-time Fourier transform, as the most commonly used linear transform, is firstly discussed. Next, the Wigner distribution, as the best known quadratic distribution, is presented. Also, the Cohen class and some specific distributions belonging to this class are considered [1, 5–7]. It is shown that the quadratic distributions are optimal for a linear frequency-modulated signal. However, if the instantaneous frequency variation within the analysis window is faster, multiwindow, or higher order distributions should be used, [14–16, 19–27]. The Hermite functions-based multiwindow approach is also discussed. Finally, highly concentrated distributions with complex-lag argument are presented. To facilitate in better understanding of the presented theoretical considerations, numerous illustrative examples have been provided.

The second part of the paper considers time-frequency-based watermarking techniques. The watermarking of digital audio, digital image, and digital video is discussed [28–37]. A short overview of some existing approaches is first given and they are related to digital audio and digital image [28–50]. Watermarking using time-frequency techniques is usually employed in either of the following two ways. The first one uses the time/space domain of the host signal to embed the watermark with specific time-frequency characteristics. The time-frequency analysis is then used for detection. The second way uses time-frequency distributions to create or embed watermark in the time-frequency domain. A flexible procedure that can be used for different kinds of signals is discussed more extensively. Therein, the watermark is shaped according to the time-frequency characteristics of the host signal. The detection is performed in the time-frequency domain. This particular approach is presented in the multidimensional form, and it is applied to digital audio, digital image, and digital video. It provides a high degree of robustness and imperceptibility. Hence, even when the watermark is very weak, a reliably detection can still be achieved. Also, the watermark gets completely hidden by the time-frequency characteristics of the host signal.

## 2. Time-Frequency Analysis

The Fourier transform provides spectral content of a signal. It has been a valuable tool in various applications. However, for nonstationary signals the Fourier transform cannot give satisfactory results since the information about frequency components variations in time is required.

A question that naturally arises at this point is whether there exist a single representation that would be ideal for any signal at hand. The answer is no, hence a number of time-frequency distributions have been introduced.

### 2.1. The Short-Time Fourier Transform

*τ*is the lag coordinate. An illustration of the STFT calculation is shown in Figure 2.

Note that the spectral content is calculated for each windowed part of the signal. The central point of the sliding window is the time instant for which the spectrum is calculated. The influence of the window size is critical, as it will be discussed later.

are the measures of duration in time and frequency, respectively. The signal should satisfy .

An important property of the STFT is its linearity. Namely, the STFT of a multicomponent signal is . Consequently, if the signal components do not intersect in the time-frequency plane the spectrogram will be equal to the sum of spectrograms of each of the signal components. This is evident from Figure 3.

### 2.2. Quadratic Time-Frequency Distributions

Quadratic time-frequency distributions have been introduced in order to improve the time-frequency resolution. Namely, they remove the spread factors for linear frequency-modulated signals. Among them, the Wigner distribution is the most commonly used. It has also been used as a base to define several interesting time-frequency distributions.

The spread factor in this case is .

In this case, the auto components are well concentrated. However, the cross-terms presence is significant. Namely, the time-frequency representation contains frequency components that do not exist within the signal itself. It could lead to a wrong analysis result.

Note that there is a trade-off between the cross-terms reduction and auto-term concentration (it depends on parameters of the kernel function). Obviously, distributions belonging to the Cohen class lie in between the two extreme cases: the spectrogram that eliminates the cross-terms with a low auto-term concentration and the Wigner distribution that provides high resolution, but with emphatic cross-terms. It is possible to obtain an ideal time-frequency concentration only if signal dependent kernels are used [8, 10].

The frequency domain finite window is denoted by . Observe that for the spectrogram and the Wigner distribution are obtained, respectively.

The calculation of the S-method is illustrated for the central point of an auto-term as well as for the point located in between the two auto components (Figures 7(a) and 7(b)). It is important to observe that the summation has to be performed only over the auto-terms. If the other terms are included, the concentration will not be improved. In addition, the noise could be also picked up. The window has to be narrower than the minimal distance between the auto-terms. If this is not the case, the interactions between the auto-terms will produce the cross-terms. Namely, as illustrated in Figure 7(b), the cross-terms appear if the window includes the summation terms marked by red color.

The adaptive S-method with a variable window adjusted to the auto-terms is introduced in [18]. However, in many applications the fixed window size of has been shown to provide very good results, since the convergence within the window is fast, and it is mostly achieved after a few summation terms.

Note that all signal components (with constant and linear frequency modulations) are well concentrated even for . By increasing , for the cross-terms start to appear (the minimal distance between the auto-terms becomes less than ).

The speech signal time-frequency resolution is improved by using the S-method.

Observe that the spread factor in the quadratic distributions will be present if the instantaneous frequency contains third and higher order phase derivatives. Hence, further concentration improvement can be obtained by using the multiwindow approach or by using higher order time-frequency distributions, as discussed below.

### 2.3. Multiwindow Time-Frequency Distributions

From Figure 10 it is obvious that the multiwindow versions of distributions outperform their standard counterparts. The multiwindow approach reduces the noise influence as well [27].

where is the order Laguerre function, and it is the Wigner distribution of the order Hermite function.

According to the previous consideration, the spread factor can be gradually reduced by increasing the number of Hermite functions, that is, by increasing the number of spectrograms in (16) and (22). Similar resolution improvements can be obtained by using polynomial time-frequency distributions, where each additional order of the distribution results in a removal of one more term within the spread factor [3].

### 2.4. Distributions with Complex-Lag Argument

For a multicomponent signal, it is calculated for each component separately, where the STFT is used to separate them [20]. This procedure can be generalized for an arbitrary distribution order [24].

Note that the Wigner distribution produces poor results for both signals, since it cannot follow the instantaneous frequency variations.

## 3. Digital Watermarking

Digital watermarking has been used to protect multimedia data. Demands in this area increase proportionally with the number of internet applications. Namely, these applications are associated with a need for copyright protection of digital audio, digital image, and digital video. Note that the cryptographic methods could be used for this purpose. However, once the data are decoded they can be unlimitedly copied. This has been one of the primary reasons for developing the watermarking techniques. The watermarking, in general, consists of embedding a secret information that can be reliably detected within the host signal. Obviously, this information should be imperceptible within the host data. Depending on the application type, the watermarking can be robust, fragile, or semifragile. The robust watermark should be resistant to various nonmalicious or malicious attacks. Nonmalicious attacks are commonly used signal processing techniques such as compression algorithms, filtering, and so forth, while the malicious attacks are the signal processing techniques that are intentionally used to remove the watermark. The fragile watermark is used to prove data authenticity. Thus, if the content of a signal has been changed, the watermark should no longer exist. The semifragile watermark should be robust to a slight modification, such as for example a certain degree of compression.

Depending on the type of host signal (speech/audio signals, image, video, etc.) various watermarking approaches are developed. Also, different domains have been used: the time domain (or the space domain), the spectral domains such as DFT, DWT, and DCT domain, and a joint time/space-frequency domain. The existing watermarking techniques are mainly based on either the time or frequency domain. However, in both cases, the time-frequency characteristics of the watermark do not correspond to the time-frequency characteristics of the host signal. It may result in the watermark being not imperceptible, because it is present in the time-frequency regions where the signal components do not exist.

### 3.1. An Overview of Some Time-Frequency-Based Watermarking Techniques

The time-frequency domain can be very efficient regarding the watermark imperceptibility and robustness. This section presents some key time-frequency-based watermarking procedures with the aim to inspire more contributions on this topic.

- (A)
Image Watermark with Specific Time-Frequency Characteristics

Observe that the Wigner distribution provides an ideal representation for this signal.

The watermark is embedded within the entire image: .

*,*, and are used. Different values of those parameters ( , and ) produce a set of projections. The additional term can be used to detect some geometrical transformations, as well. Note that the detector has the form of the Radon-Wigner distribution, which ensures that the energy of the watermark is distributed over the hyper plane defined by ( is the phase function of the watermark). In order to make a decision weather the watermark exists in the image or not, the maxima of the Radon Wigner distribution

are compared with an assumed reference threshold. Also, multiple chirp watermarks with small and randomly chosen amplitude are used to increase flexibility of the proposed procedure. The parameters of the chirp signal as well as the random sequence that defines the amplitudes of chirp signals serve as the watermark key. Since the watermark is embedded within the entire image in the space domain, a proper masking that provides imperceptibility should be applied. An analysis of the performances giving an estimation of the detectable watermark amplitude level is provided in [38]. The robustness is tested on various attack, some being a median filter, geometrical transformations (translation, rotation and cropping simultaneously applied), a high-pass filter, local notch filter, and Gaussian noise.

This provides a possibility to use the watermark that cannot be detected by considering a single block only. Thus, in such case it would be necessary to integrate all of them over the entire image. Note that it is also possible to generate different chirps for different blocks instead of using the same chirp for all blocks. It would make the detection even more difficult for unauthorized users.

where is the unmarked compressed block. The watermark is completely removed by compression if is obtained. The quality of the proposed technique is tested on the image Lena, and it is proven that, for this case, it outperforms the standard spread spectrum technique.

where the watermark is a Gaussian white noise with the variance
. The watermark key consists of the watermark sequence and the angles (
_{1},
_{2}). Thus, the algorithm provides two more degrees of freedom, and it offers more possibility to generate watermarks. The watermarking procedure is tested on various images and attacks.

(A.4) Barkat and Sattar have proposed a fragile watermarking procedure for image authentication [43]. The watermark with a particular time-frequency signature is inserted in the image pixels. Although, in general, pixels (according to the image size) exist, a significantly lower number of them is used. The pixels location can be chosen arbitrarily. The authors have used diagonal pixels, modulated by a pseudonoise sequence as a secret key. Various frequency-modulated nonstationary signals can be a watermark, as well. However, the features that could be easily identified should be used. Consequently, different time-frequency distributions should be used for watermark detection. Barkat and Sattar have used a quadratic frequency-modulated signal. It is detected by using the Wigner distribution. The proposed scheme is tested on the following attacks: cropping, translation, JPEG compression, and scaling. Very week and imperceptible attacks were applied (e.g., JPEG with 99% quality is used). It is shown that the watermark cannot be identified after these attacks.

*(B) Watermark Created in the Time-Frequency Domain*

However, the previous equation holds only if the two-dimensional function (45) is a valid Wigner distribution. Namely, it is well known that any two-dimensional function cannot be the Wigner distribution. It introduces a very restrictive condition on the function . In the proposed method it is determined by using the time-frequency representation of the corresponding row and taking the middle frequency region.

Al-khassaweneh and Aviyente have suggested a nonblind detection procedure. Namely, the second part of the function in (46) that depends on the watermark is selected. The detection is performed by using the standard correlation detection. A threshold that provides a minimal probability of error is derived. The proposed method is tested on different images and under various attacks. The average probability of error was found to be 0.03.

(B.2) Foo et al. in [48] have defined a method for digital audio watermarking based on the time-frequency domain. Here the audio frames are changed, so that the logical value of 1 is assigned. If the original frame is lengthened or shortened, the logical value 1 is assigned, otherwise the "normal frames" correspond to the logical value 0. The watermark is a sequence obtained as a binary code of the alphabet letters, converted to the ASCII code (the example with the binary code 010001100101001101010111 for the letters FSW is used). The crucial part of this method is the selection of frames that will be lengthened or shortened (the frame size of 1024 samples is used). The frames with signal energy level above the masking threshold are selected (the psychoacoustic model is used to determine the masking threshold in each subband). The frames length is changed by adding or removing samples with amplitudes that do not exceed the masking threshold. Four samples are added or removed within the frame of 1024 samples. It ensures that a perceptual distortion will not appear. In order to preserve the total length of the watermarked audio signal, the same number of the lengthened and shortened frames is used. The pair of frames called Diamond frames is used to represent the binary 1, while the logical values 0 are assigned to the unaltered frames.

The detection procedure is nonblind, that is, the original signal is required. A significant difference between the watermarked and the original signals will appear only if a pair of changed frames exists. Thus, it is used for logical values detection. The proposed watermarking scheme has been tested on various musical signals, as well as on a speech signal, and a set of different attacks has been applied (filtering, resampling, noise, cropping, and MP3 compression). Although the results vary for different signals and attacks, in general they are good. The worst results are obtained for the rock and pop music signals with MP3 compression. However, in all cases the owner can be identified.

The probability of energy for each frequency (within a window used for the spectrogram calculation) is denoted by , while is the maximum frequency. A half of the maximum entropy is taken as a threshold between noise-like and tone-like characteristics. If the entropy is lower than it is considered as a tone-like, otherwise it is a noise-like characteristic.

Finally, after the watermark is modulated and shaped, it is embedded in the time domain as .

A simple watermark detection procedure is applied. First, demodulation is performed by using the time-varying carrier, and then the watermark is detected by using the standard correlation procedure with the pseudonoise sequence.

The proposed method has been tested on several music files. It has been shown that, under various attacks, the bit error rates are mostly between 0.02 and 0.08.

(B.4) An interesting audio watermarking approach based on linear chirps has been proposed in [47]. The watermark is created as a chirp signal, which is perceptually shaped according to the host signal samples. Different chirp rates, each representing a unique watermark message, produce different slopes in the time-frequency domain. The efficient time-frequency representation is obtained by using the Wigner distribution. The extracted chirps are postprocessed in the time-frequency plane by an optimal line detection method based on the Hough-Radon transform. It can correctly estimate the slope of the watermark signal despite the broken lines caused by attacks. The simulation results show that the Hough-Radon transform applied to a time-frequency distribution can detect the watermark message correctly at bit error rates up to 20%.

### 3.2. Watermaking Approach Based on the Time-Frequency-Shaped Watermark

The approach that will be presented can be used either for audio signals or images [41, 42]. Thus, the embedding and detection procedures for both kinds of signals will be defined and discussed simultaneously, by using the multidimensional notation.

In order to ensure imperceptibility constraints, the watermark should be modeled according to the time-frequency characteristics of the signal components. The concept of nonstationary multidimensional filtering [52] is adapted and used to create a watermark with time-frequency characteristics that correspond to the characteristics of the host signal. The corresponding algorithm consists of the following steps:

()selection of the nonstationary parts of signal suitable for watermark embedding;

()watermark modeling according to the multidimensional time-frequency characteristics of the host signal;

()watermark embedding and watermark detection procedure within the multidimensional time-frequency domain.

Multidimensional time-frequency distributions are employed in order to determine the nonstationary regions. As it will be shown later, the S-method can be efficiently used to analyze dynamics of the regions of speech signals and images. Although the cross-terms are usually undesirable in the time-frequency analysis, they have found to be useful in watermarking. Namely, they may increase performances of a speech watermark detector, and also, increase the efficiency of dynamic regions selection within an image.

where is the short-time Fourier transform of a multidimensional random sequence . The function contains the information about the components within the region . It is used to create the watermark that will be adjusted to these components. Thus, we may start with an arbitrary random multidimensional sequence and, by using , its multidimensional time-frequency characteristic is modeled.

A value of between 0 and 1 is taken.

where and are the short-time Fourier transforms of the multidimensional watermarked data, the host data, and the watermark, respectively.

The multidimensional time-frequency domain-based detector provides a low probability of error, even when the number of watermarked samples in the signal domain is small.

#### 3.2.1. Digital Audio Signal

The watermark is embedded in the time domain: . The time-frequency representation of the watermark is shown in Figure 12(c).

As expected, the time-frequency characteristics of the watermark follow those components of the speech signal. Consequently, the watermark is inaudible within the speech signal.

In this case, the fourth-order complex-lag distribution is more appropriate for the region selection than the S-method, because it better follows the frequency variations in the signal (Figures 13(a) and 13(b)). Thus, by using this distribution an inaudible watermark is created.

The second term in (60) is the result of cross-terms.

Note that this form of detector can be used in other existing detector structures.

is used. The mean value and standard deviation of the detector response are denoted by and . Indices and indicate the right and the wrong keys, respectively.

Efficiency of the proposed procedure is demonstrated on various examples. The results for speech signals with maximum frequencies of 4 kHz and 11,025 kHz are presented in [41]. This approach provides a reliable detection for a high SNR (SNR = 32 dB has been used) and under various attacks. The watermark sequence was created by using a pseudorandom Gaussian sequence of 1000 samples.

The probability of error was of order 10^{−7} for: MP3 (constant bit rate 8 kbps and variable bit rate 75–120 kbps are considered), delay monolight echo (180 ms, mixing 20%), echo 200 ms, deep flutter (deep 10, sweeping rate 5 kHz), amplitude (normalize 100%), and additive Gaussian noise (SNR = −35 dB). The worst case is obtained for pitch scaling
and it is of order 10^{−5}. The results for other attacks (time stretch
wow delay 20%, wow delay 10% and bright flutter, MP3 variable bit rate 40–50 kbps) are of order 10^{−6}.

#### 3.2.2. Digital Image

By increasing the size of a two-dimensional window, the cross-terms start to appear. Thus, when compared to the spectrogram, the number of frequency components increases, hence making the region characterization easier. A pixel that belongs to the dynamic region can be selected by using the following procedure.

()The S-method is calculated for a window (windows of size up to are used). The middle frequency range is used.

()The energy floor is obtained by using the experimentally determined .

where , while is the total number of the points within the region .

A two-dimensional pseudorandom sequence is used.

This procedure is tested on several images (Lena, Peppers, Boat, F16, and Barbara), under various attacks (JPEG80-JPEG40, Median , Median , Average , Impulse noise, Gaussian noise, Lightening, and Darkening). The PSNR was around 50 dB. The number of the selected pixels varied from 3304 for F16 to 7833 for Barbara. The probability of error was compared with the standard DCT-based procedure (with different detector forms), where 22050 coefficients are used. It was shown that the proposed procedure significantly outperforms the standard DCT procedures.

#### 3.2.3. Digital Video

where , while is a constant. The stationarity of the selected pixels, along the time axis, is examined by using the one-dimensional S-method. The experiments show that the minimal number of pixels for reliable watermark detection is about 600. This can be easily achieved, even for a very short video sequence (note that more than 2500 stationary pixels are obtained for a signal of duration of 2 s in the example provided in [42]). This approach was tested under the presence of MPEG4 compression. The obtained probabilities of errors were found to be within the range .

## 4. Conclusion

An overview of most important time-frequency analysis techniques is presented. An appropriate distribution selection procedure for a specific type of signal is discussed. Time-frequency-based watermarking algorithms for digital audio, digital image, and video are reviewed, as well. The watermark is either a signal with specific time-frequency characteristics or a pseudonoise sequence shaped according to the time-frequency characteristics of the host signal. The main advantages of the time-frequency domain over the Fourier, DCT, and signal domain are emphasized. Finally, the presented theory could be used to generalize the existing watermarking approaches defined in either the Fourier or the DCT domain.

## Declarations

### Acknowledgments

The author is very thankful to Dr. Irena Orović and Professor Victor Sučić for their help and useful suggestions during the work on this paper.

## Authors’ Affiliations

## References

- Cohen L: Time-frequency distributions—a review.
*Proceedings of the IEEE*1989, 77(7):941-981. 10.1109/5.30749View ArticleGoogle Scholar - Hlawatsch F, Boudreaux-Bartels GF: Linear and quadratic time-frequency signal representations.
*IEEE Signal Processing Magazine*1992, 9(2):21-67. 10.1109/79.127284View ArticleGoogle Scholar - Boashash B, Ristić B: Polynomial time-frequency distributions and time-varying higher order spectra: application to the analysis of multicomponent FM signals and to the treatment of multiplicative noise.
*Signal Processing*1998, 67(1):1-23. 10.1016/S0165-1684(98)00018-8View ArticleMATHGoogle Scholar - Boashash B:
*Time-Frequency Analysis and Processing*. Elsevier, Amsterdam, The Netherlands; 2003.MATHGoogle Scholar - Choi H, Williams WJ: Improved time-frequency representation of multicomponent signals using exponential kernels.
*IEEE Transactions on Acoustics, Speech, and Signal Processing*1989, 37(6):862-871. 10.1109/ASSP.1989.28057View ArticleGoogle Scholar - Zhao Y, Atlas LE, Marks RJ: The use of cone-shaped kernels for generalized time-frequency representations of nonstationary signals.
*IEEE Transactions on Acoustics, Speech, and Signal Processing*1990, 38(7):1084-1091. 10.1109/29.57537View ArticleGoogle Scholar - Stanković L: Auto-term representation by the reduced interference distributions: a procedure for kernel design.
*IEEE Transactions on Signal Processing*1996, 44(6):1557-1563. 10.1109/78.506622View ArticleGoogle Scholar - Baraniuk RG, Jones DL: A signal-dependent time-frequency representation. Optimal kernel design.
*IEEE Transactions on Signal Processing*1993, 41(4):1589-1602. 10.1109/78.212733View ArticleMATHGoogle Scholar - Hlawatsch F, Urbanke RL: Bilinear time-frequency representations of signals: the shift-scale invariant class.
*IEEE Transactions on Signal Processing*1994, 42(2):357-366. 10.1109/78.275608View ArticleGoogle Scholar - Baraniuk RG, Jones DL: Signal-dependent time-frequency analysis using a radially Gaussian kernel.
*Signal Processing*1993, 32(3):263-284. 10.1016/0165-1684(93)90001-QView ArticleMATHGoogle Scholar - Amin MG, Williams WJ: High spectral resolution time-frequency distribution kernels.
*IEEE Transactions on Signal Processing*1998, 46(10):2796-2804. 10.1109/78.720381View ArticleGoogle Scholar - Bastiaans MJ, Alieva T, Stanković L: On rotated time-frequency kernels.
*IEEE Signal Processing Letters*2002, 9(11):378-381. 10.1109/LSP.2002.805118View ArticleGoogle Scholar - Boashash B: Estimating and interpreting the instantaneous frequency of a signal—part 1: fundamentals.
*Proceedings of the IEEE*1992, 80(4):520-538. 10.1109/5.135376View ArticleGoogle Scholar - Barkat B, Boashash B: Design of higher order polynomial Wigner-Ville distributions.
*IEEE Transactions on Signal Processing*1999, 47(9):2608-2611. 10.1109/78.782225View ArticleMATHGoogle Scholar - Viswanath G, Sreenivas TV: IF estimation using higher order TFRs.
*Signal Processing*2002, 82(2):127-132. 10.1016/S0165-1684(01)00168-2View ArticleMATHGoogle Scholar - Stanković L: A multitime definition of the Wigner higher order distribution: L-Wigner distribution.
*IEEE Signal Processing Letters*1994, 1(7):106-109. 10.1109/97.311805View ArticleGoogle Scholar - Stanković L: A method for time-frequency analysis.
*IEEE Transactions on Signal Processing*1994, 42(1):225-229. 10.1109/78.258146View ArticleGoogle Scholar - Stanković S, Stanković L: An architecture for the realization of a system for time-frequency signal analysis.
*IEEE Transactions on Circuits and Systems II*1997, 44(7):600-604. 10.1109/82.598433View ArticleMATHGoogle Scholar - Stanković S, Stanković L: Introducing time-frequency distribution with a "complex-time" argument.
*Electronics Letters*1996, 32(14):1265-1267. 10.1049/el:19960849View ArticleGoogle Scholar - Stanković L: Time-frequency distributions with complex argument.
*IEEE Transactions on Signal Processing*2002, 50(3):475-486. 10.1109/78.984717View ArticleGoogle Scholar - Cornu C, Stanković S, Ioana C, Quinquis A, Stanković L: Generalized representation of phase derivatives for regular signals.
*IEEE Transactions on Signal Processing*2007, 55(10):4831-4838.MathSciNetView ArticleGoogle Scholar - Stanković S, Orovic I, Ioana C: Effects of Cauchy integral formula on the precision of the IF estimation.
*IEEE Signal Processing Letters*2009, 16(4):327-330.View ArticleGoogle Scholar - Morelande M, Senadji B, Boashash B: Complex-lag polynomial Wigner-Ville distribution.
*Proceedings of IEEE Speech and Image Technologies for Computing and Telecommunications (TENCON '97), December 1997, Brisbane, Australia*1: 43-46.View ArticleGoogle Scholar - Stanković S, Žarić N, Orović I, Ioana C: General form of time-frequency distribution with complex-lag argument.
*Electronics Letters*2008, 44(11):699-701. 10.1049/el:20080902View ArticleGoogle Scholar - Frazer G, Boashash B: Multiple window spectrogram and time-frequency distributions.
*Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '94), 1994*4: 193-296.Google Scholar - Çakrak F, Loughlin PJ: Multiwindow time-varying spectrum with instantaneous bandwidth and frequency constraints.
*IEEE Transactions on Signal Processing*2001, 49(8):1656-1666. 10.1109/78.934135View ArticleGoogle Scholar - Bayram M, Baraniuk RG: Multiple window time-frequency analysis.
*Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis, June 1996*173-176.Google Scholar - Cox IJ, Miller ML, Bloom JA:
*Digital Watermarking*. Academic Press, New York, NY, USA; 2002.Google Scholar - Barni M, Bartolini F:
*Watermarking Systems Engineering*. Marcel Dekker, New York, NY, USA; 2004.Google Scholar - Special issue on "Identification and protection of multimedia information" Proceedings of the IEEE 1999., 87(7):Google Scholar
- Muharemagic E, Furht B: Survey of watermarking techniques and applications. In Multimedia Watermarking Techniques and Applications. Edited by: Furht B, Kirovski D. Auerbach Publication; 2006:91-130.Google Scholar
- Kirovski D, Malvar HS: Spread-spectrum watermarking of audio signals.
*IEEE Transactions on Signal Processing*2003, 51(4):1020-1033. 10.1109/TSP.2003.809384MathSciNetView ArticleGoogle Scholar - Steinebach M, Dittmann J: Watermarking-based digital audio data authentication.
*EURASIP Journal on Applied Signal Processing*2003, 2003(10):1001-1015. 10.1155/S1110865703304081View ArticleGoogle Scholar - Nikolaidis A, Pitas I: Asymptotically optimal detection for additive watermarking in the DCT and DWT domains.
*IEEE Transactions on Image Processing*2003, 12(5):563-571. 10.1109/TIP.2003.810586View ArticleGoogle Scholar - Hernández JR, Amado M, Pérez-González F: DCT-domain watermarking techniques for still images: detector performance analysis and a new structure.
*IEEE Transactions on Image Processing*2000, 9(1):55-68. 10.1109/83.817598View ArticleGoogle Scholar - Briassouli A, Strintzis MG: Locally optimum nonlinearities for DCT watermark detection.
*IEEE Transactions on Image Processing*2004, 13(12):1604-1617. 10.1109/TIP.2004.837516View ArticleGoogle Scholar - Cheng Q, Huang TS: An additive approach to transform-domain information hiding and optimum detection structure.
*IEEE Transactions on Multimedia*2001, 3(3):273-284. 10.1109/6046.944472View ArticleGoogle Scholar - Stanković S, Djurović I, Pitas L: Watermarking in the space/spatial-frequency domain using two-dimensional Radon-Wigner distribution.
*IEEE Transactions on Image Processing*2001, 10(4):650-658. 10.1109/83.913599View ArticleMATHGoogle Scholar - Djurović I, Stanković S, Pitas I: Digital watermarking in the fractional Fourier transformation domain.
*Journal of Network and Computer Applications*2001, 24(2):167-173. 10.1006/jnca.2000.0128View ArticleMATHGoogle Scholar - Wickens T:
*Elementary Signal Detection Theory*. Oxford University Press, Oxford, UK; 2002.Google Scholar - Stanković S, Orović I, Žarić N: Robust speech watermarking procedure in the time-frequency domain.
*EURASIP Journal on Advances in Signal Processing*2008, 2008:-9.Google Scholar - Stanković S, Orović I, Žarić N: An application of multidimensional time-frequency analysis as a base for the unified watermarking approach.
*IEEE Transactions on Image Processing*2010, 19(3):736-745.MathSciNetView ArticleGoogle Scholar - Barkat B, Sattar F: A new time-frequency based private fragile watermarking scheme for image authentication.
*Proceedings of the 7th International Symposium on Signal Processing and Its Applications, July 2003*2: 363-366.Google Scholar - Mobasseri BG, Zhang Y, Amin MG, Dogahe BM: Designing robust watermarks using polynomial phase exponentials.
*Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '05), March 2005*833-836.Google Scholar - Kutter M, Winkler S: A vision-based masking model for spread-spectrum image watermarking.
*IEEE Transactions on Image Processing*2002, 11(1):16-25. 10.1109/83.977879View ArticleGoogle Scholar - Esmaili S, Krishnan S, Raahemifar K: Audio watermarking using time-frequency characteristics.
*Canadian Journal of Electrical and Computer Engineering*2003, 28(2):57-61. 10.1109/CJECE.2003.1532509View ArticleGoogle Scholar - Erküçük S, Krishnan S, Zeytinoǧlu M: A robust audio watermark representation based on linear chirps.
*IEEE Transactions on Multimedia*2006, 8(5):925-926.View ArticleGoogle Scholar - Foo SW, Ho SM, Ng LM: Audio watermarking using time-frequency compression expansion.
*Proceedings of IEEE International Symposium on Cirquits and Systems (ISCAS '04), May 2004*201-204.Google Scholar - Al-khassaweneh M, Aviyente S: A time-frequency based perceptual and robust watermarking scheme.
*Proceedings of 13th European Signal Processing Conference (EUSIPCO '05), September 2005, Antalya, Turkey*Google Scholar - Erküçük S:
*Time-frequency analysis of spread spectrum based communication and audio watermarking systems, Ph.D. thesis*. 2003.Google Scholar - Orović I, Stanković S, Thayaparan T, Stanković L: Multiwindow S-method for instantaneous frequency estimation and its application in radar signal analysis. IET Signal Processing 2010, 90(5):-7.Google Scholar
- Stanković L, Stanković S, Djurović I: Space/spatial-frequency analysis based filtering.
*IEEE Transactions on Signal Processing*2000, 48(8):2343-2352. 10.1109/78.852015MathSciNetView ArticleMATHGoogle Scholar - Stanković S: About time-variant filtering of speech signals with time-frequency distributions for hands-free telephone systems.
*Signal Processing*2000, 80(9):1777-1785. 10.1016/S0165-1684(00)00087-6View ArticleMATHGoogle Scholar - Stanković S, Stanković L, Uskokovic Z: On the local frequency, group shift, and cross-terms in some multidimensional time-frequency distributions: a method for multidimensional time-frequency analysis.
*IEEE Transactions on Signal Processing*1995, 43(7):1719-1724. 10.1109/78.398736View ArticleGoogle Scholar

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