Showing papers on "Time–frequency analysis published in 2006"
TL;DR: A novel approach to harmonic and inter Harmonic analysis based on the "subspace" methods is proposed, which do not show the disadvantages of the traditional tools and allow exact estimation of the interharmonic frequencies.
Abstract: The spectrum-estimation methods based on the Fourier transform suffer from the major problem of resolution. The methods were developed and are mostly applied for periodic signals under the assumption that only harmonics are present and the periodicity intervals are fixed, while periodicity intervals in the presence of interharmonics are variable and very long. A novel approach to harmonic and interharmonic analysis based on the "subspace" methods is proposed. Min-norm and music harmonic retrieval methods are examples of high-resolution eigenstructure-based methods. Their resolution is theoretically independent of the signal-to-noise ratio (SNR). The Prony method as applied for parameter estimation of signal components was also tested in the paper. Both the high-resolution methods do not show the disadvantages of the traditional tools and allow exact estimation of the interharmonic frequencies. To investigate the methods, several experiments were carried out using simulated signals, current waveforms at the output of an industrial frequency converter, and current waveforms during out-of-step operation of a synchronous generator. For comparison, similar experiments were repeated using the fast Fourier transform (FFT). The comparison proved the superiority of the new methods.
125 citations
TL;DR: It has been shown that the chirplet signal decomposition algorithm performs robustly, yields accurate echo estimation, and results in SNR enhancements, and the algorithm is efficient and successful in high-fidelity signal representation.
Abstract: In ultrasonic imaging systems, the patterns of detected echoes correspond to the shape, size, and orientation of the reflectors and the physical properties of the propagation path. However, these echoes often are overlapped due to closely spaced reflectors and/or microstructure scattering. The decomposition of these echoes is a major and challenging problem. Therefore, signal modeling and parameter estimation of the nonstationary ultrasonic echoes is critical for image analysis, target detection, arid object recognition. In this paper, a successive parameter estimation algorithm based on the chirplet transform is presented. The chirplet transform is used not only as a means for time-frequency representation, but also to estimate the echo parameters, including the amplitude, time-of-arrival, center frequency, bandwidth, phase, and chirp rate. Furthermore, noise performance analysis using the Cramer Rao lower bounds demonstrates that the parameter estimator based on the chirplet transform is a minimum variance and unbiased estimator for signal-to-noise ratio (SNR) as low as 2.5 dB. To demonstrate the superior time-frequency and parameter estimation performance of the chirplet decomposition, ultrasonic flaw echoes embedded in grain scattering, and multiple interfering chirplets emitted by a large, brown bat have been analyzed. It has been shown that the chirplet signal decomposition algorithm performs robustly, yields accurate echo estimation, and results in SNR enhancements. Numerical and analytical results show that the algorithm is efficient and successful in high-fidelity signal representation
109 citations
TL;DR: The analysis shows that this method can provide additional insight into the interpretation and processing of radar signals, with respect to the traditional Fourier transform based methods currently used by the HFSWRs.
Abstract: This paper presents a new approach to the time-frequency signal analysis and synthesis, using the eigenvalue decomposition method. It is based on the S-method, the time-frequency representation that can produce a distribution equal or close to a sum of the Wigner distributions of individual signal components. The new time-frequency signal decomposition method is evaluated on the simulated and experimental high-frequency surface-wave radar (HFSWR) data. Results demonstrate that it provides an effective way for analyzing and detecting maneuvering air targets with significant velocity changes, including target signal separation from the heavy clutter. The analysis shows that this method can provide additional insight into the interpretation and processing of radar signals, with respect to the traditional Fourier transform based methods currently used by the HFSWRs. The proposed method could also be used in other signal processing applications
106 citations
TL;DR: In this article, two independently emerging time-frequency transformations in Civil Engineering, namely, the wavelet transform and empirical mode decomposition with Hilbert transform (EMD+HT), are discussed.
Abstract: Two independently emerging time-frequency transformations in Civil Engineering, namely, the wavelet transform and empirical mode decomposition with Hilbert transform (EMD+HT) , are discussed in this study. Their application to a variety of nonstationary and nonlinear signals has achieved mixed results, with some comparative studies casting significant doubt on the wavelet’s suitability for such analyses. Therefore, this study shall revisit a number of applications of EMD+HT in the published literature, offering a different perspective to these commentaries and highlighting situations where the two approaches perform comparably and others where one offers an advantage. As this study demonstrates, much of the differing performance previously observed is attributable to EMD+HT representing nonlinear characteristics solely through the instantaneous frequency, with the wavelet relying on both this measure and the instantaneous bandwidth. Further, the resolutions utilized by the two approaches present a seconda...
78 citations
TL;DR: Application of the TVOPS-VFCDM to renal blood flow data indicates some promise of a quantitative approach to understanding the dynamics of renal autoregulatory mechanisms as well as a possible approach to quantitatively discriminating between different strains of rats.
Abstract: A high resolution approach to estimating time-frequency spectra (TFS) and associated amplitudes via the use of variable frequency complex demodulation (VFCDM) is presented. This is a two-step procedure in which the previously developed time-varying optimal parameter search (TVOPS) technique is used to obtain TFS, followed by using the VFCDM to obtain even greater TFS resolution and instantaneous amplitudes associated with only the specific frequencies of interest. This combinational use of the TVOPS and the VFCDM is termed the TVOPS-VFCDM. Simulation examples are provided to demonstrate the performance of the TVOPS-VFCDM for high resolution TFS as well as instantaneous amplitude estimation. The simulation results show that the TVOPS-VFCDM approach provides the highest resolution and most accurate amplitude estimates when compared to the smoothed pseudo Wigner–Ville, continuous wavelet transform and Hilbert–Huang transform methods. Application of the TVOPS-VFCDM to renal blood flow data indicates some promise of a quantitative approach to understanding the dynamics of renal autoregulatory mechanisms as well as a possible approach to quantitatively discriminating between different strains of rats.
76 citations
TL;DR: Comparative results between the proposed Short-Time FChT and popular time–frequency techniques reveal an improvement in spectral and time-frequency representation.
74 citations
TL;DR: A comparative study of four representative time-frequency analysis techniques commonly employed for non-stationary signal processing demonstrates that selecting appropriate signal processing technique can significantly affect defect identification and consequently, improve the reliability of bearing health monitoring.
Abstract: Signals generated by transient vibrations in rolling bearings due to structural defects are non-stationary in nature, and reflect upon the operation condition of the bearing. Consequently, effective processing of non-stationary signals is critical to bearing health monitoring. This paper presents a comparative study of four representative time-frequency analysis techniques commonly employed for non-stationary signal processing. The analytical framework of the short-time Fourier transform, wavelet transform, wavelet packet transform, and Hilbert-Huang transform are first presented. The effectiveness of each technique in detecting transient features from a time-varying signal is then examined, using an analytically formulated test signal. Subsequently, the performance of each technique is experimentally evaluated, using realistic vibration signals measured from a bearing test system. The results demonstrate that selecting appropriate signal processing technique can significantly affect defect identification and consequently, improve the reliability of bearing health monitoring.
70 citations
TL;DR: Using this proposed model, a joint multipath-scale diversity can be achieved over a dyadic time-scale framework in wideband wireless systems by properly designing the signaling and reception schemes using wavelet techniques.
Abstract: Wideband time-varying systems can be found in many applications, including underwater acoustics and ultra-wideband technologies The time variation due to Doppler scaling effects, coupled with dispersive scattering due to multipath propagation, can severely limit the performance of wideband systems Just as the discrete time-frequency model can efficiently improve narrowband processing, a discrete time-scale system characterization is important in processing wideband time-varying systems In this paper, a time-scale model is proposed as a discrete characterization of wideband time-varying systems This representation decomposes a wideband system output into discrete time shifts and Doppler scalings on the input signal, weighted by a smoothed and sampled version of the wideband spreading function The proposed transform-based approach uses the Mellin transform that is inherently matched to scalings to geometrically sample the scale parameter and the Fourier transform to arithmetically sample the time-delay parameter Using this proposed model, and by properly designing the signaling and reception schemes using wavelet techniques, a joint multipath-scale diversity can be achieved over a dyadic time-scale framework in wideband wireless systems The simulation results demonstrate that, based on the proposed model, performance can be increased by exploiting the diversity intrinsically afforded by the wideband system
60 citations
TL;DR: The authors propose the use of "cheap methods" to find an approximation for the inverse Gabor frame matrix, based on (double) preconditioning, and obtain good approximations of the true dual Gabor atom at low computational costs.
Abstract: The authors present an application of the general idea of preconditioning in the context of Gabor frames. While most (iterative) algorithms aim at a more or less costly exact numerical calculation of the inverse Gabor frame matrix, we propose here the use of "cheap methods" to find an approximation for it, based on (double) preconditioning. We thereby obtain good approximations of the true dual Gabor atom at low computational costs. Since the Gabor frame matrix commutes with certain time-frequency shifts, it is natural to make use of diagonal and circulant preconditioners sharing this property. Part of the efficiency of the proposed scheme results from the fact that all the matrices involved share a well-known block matrix structure. At least, for the smooth Gabor atoms typically used, the combination of these two preconditioners leads consistently to good results. These claims are supported by numerical experiments in this paper. For numerical evaluations we introduce two new matrix norms, which can be calculated efficiently by exploiting the structure of the frame matrix
57 citations
TL;DR: The authors explain the limits of classical methods founded exclusively on the time or frequency basis and answer those limits with the windowed Fourier transform and the wavelet transform methods, both of which are founded on timefrequency bases.
Abstract: The authors present a practical guide for studying nonstationary data on human motor behavior in a time-frequency representation. They explain the limits of classical methods founded exclusively on the time or frequency basis and then answer those limits with the windowed Fourier transform and the wavelet transform (WT) methods, both of which are founded on time-frequency bases. The authors stress an interest in the WT method because it permits access to the whole complexity of a signal (in terms of time, frequency, amplitude, and phase). They then show that the WT method is well suited for the analysis of the interaction between two signals, particularly in human movement studies. Finally, to demonstrate its practical applications, the authors apply the method to real data.
53 citations
TL;DR: This research applies the chirplet as a tool to analyze dispersive wave signals based on a dispersion model and demonstrates the effectiveness and robustness of this algorithm on real, experimentally measured Lamb wave signals by an adaption of a correlation technique developed in previous research.
Abstract: Time-frequency representations, like the spectrogram or the scalogram, are widely used to characterize dispersive waves. The resulting energy distributions, however, suffer from the uncertainty principle, which complicates the allocation of energy to individual propagation modes (especially when the dispersion curves of these modes are close to each other in the time-frequency domain). This research applies the chirplet as a tool to analyze dispersive wave signals based on a dispersion model. The chirplet transform, a generalization of both the wavelet and the short-time Fourier transform, enables the extraction of components of a signal with a particular instantaneous frequency and group delay. An adaptive algorithm identifies frequency regions for which quantitative statements can be made about an individual mode’s energy, and employs chirplets (locally adapted to a dispersion curve model) to extract the (proportional) energy distribution of that single mode from a multimode dispersive wave signal. The ...
TL;DR: An orthogonal linear chirp modulation scheme that is based on assigning different users with optimally designed parameters in order to reduce multiple-access interference is designed and improved performance when compared with frequency-shift-keying (FSK) modulation is demonstrated.
Abstract: We propose the use of time-varying (TV) signaling in modulation schemes to provide multiuser detection and multipath diversity in TV wireless channels. Specifically, we design an orthogonal linear chirp modulation scheme that is based on assigning different users with optimally designed parameters in order to reduce multiple-access interference. We also derive conditions on the parameters of the modulation signals to achieve multipath diversity. Furthermore, we propose the use of TV pilot signals with nonlinear instantaneous frequency and matched time-frequency (TF) techniques to estimate fast-fading channels with unknown state information. The proposed algorithm simplifies to the estimation of the parameters of multiple linear chirps, which we perform using the modified matching pursuit decomposition. We compare our estimation method with the use of pilot signals with linear instantaneous frequency, which we implement using the reassigned spectrogram. The proposed modulation scheme is applied to a frequency-hopped code-division multiple-access system for which we demonstrate improved performance when compared with frequency-shift-keying (FSK) modulation due to the designed multipath diversity and low multiple-access interference. Our simulations also demonstrate the increased estimation performance when pilot signals with nonlinear structures are used instead of linear structured ones to estimate TV channel parameters
TL;DR: In this paper, it was shown that a sufficient and necessary condition for H e i Θ ( s ) = − i e i π d Θ( s ) is a harmonic measure on the line.
TL;DR: This paper investigates the benefits of using time-frequency analysis in such situations, for both waveform retrieval and imaging in the presence of low signal levels, using the short-term Fourier transform, the wavelet transform, and the Wigner-Ville distribution.
Abstract: Air-coupled ultrasound has been used for the nondestructive evaluation of concrete, using broad bandwidth electrostatic transducers and chirp excitation. This paper investigates the benefits of using time-frequency analysis in such situations, for both waveform retrieval and imaging in the presence of low signal levels. The use of the short-term Fourier transform, the wavelet transform, and the Wigner-Ville distribution all are considered, in which accurate tracking of the ultrasonic chirp signals is demonstrated. The Hough transform then is applied as a filter. An image of a steel reinforcement bar in concrete has been produced to illustrate this approach.
TL;DR: In this paper, a low strain integrity test is adopted to assess the quality of cast-in situ reinforcement concrete piles with high slenderness ratios, and a new proposed numerical signal process method is applied to explore the time-frequency component of the testing result which shows better resolution than traditional methods.
TL;DR: The Hilbert Huang transform (HHT) is introduced as an efficient means for analysis of bioacoustical signals and shows that HHT is a viable alternative to the wavelet transform.
Abstract: While marine mammals emit variant signals (in time and frequency), the Fourier spectrogram appears to be the most widely used spectral estimator. In certain cases, this approach is suboptimal, particularly for odontocete click analysis and when the signal-to-noise ratio varies during the continuous recordings. We introduce the Hilbert Huang transform (HHT) as an efficient means for analysis of bioacoustical signals. To evaluate this method, we compare results obtained from three time-frequency representations: the Fourier spectrogram, the wavelet transform, and the Hilbert Huang transform. The results show that HHT is a viable alternative to the wavelet transform. The chosen examples illustrate certain advantages. (1) This method requires the calculation of the Hilbert transform; the time-frequency resolution is not restricted by the uncertainty principle; the frequency resolution is finer than with the Fourier spectrogram. (2) The original signal decomposition into successive modes is complete. If we were to multiply some of these modes, this would contribute to attenuate the presence of noise in the original signal and to being able to select pertinent information. (3) Frequency evolution for each mode can be analyzed as one-dimensional (1D) signal. We not need a complex 2D post-treatment as is usually required for feature extraction.
14 Jun 2006
TL;DR: In this paper, the authors proposed a new technique, time-frequency domain average, which combines the time domain average and wavelet transformation together to extract the periodic waveforms at different scales from noisy vibration signals.
Abstract: The vibration signal of a gearbox carries the signature of the fault in the gears, and early fault detection of the gearbox is possible by analyzing the vibration signal using different signal processing techniques. Time domain average can extract the periodic waveforms of a noisy vibration signal, whereas wavelet transformation is able to characterize the local features of the signal in different scales. This paper proposes a new technique, time-frequency domain average, which combines the time domain average and wavelet transformation together to extract the periodic waveforms at different scales from noisy vibration signals. The technique efficiently cleans up noise and detects both local and distributed faults simultaneously. A pilot plant case study has been presented to demonstrate the efficacy of the proposed technique.
TL;DR: A method for blind separation of convolutive mixtures of speech signals, based on the joint diagonalization of the time varying spectral matrices of the observation records, is presented, compared with other approaches and results on real world recordings demonstrate superior performances of the proposed algorithm.
Abstract: This paper presents a method for blind separation of convolutive mixtures of speech signals, based on the joint diagonalization of the time varying spectral matrices of the observation records. The main and still largely open problem in a frequency domain approach is permutation ambiguity. In an earlier paper of the authors, the continuity of the frequency response of the unmixing filters is exploited, but it leaves some frequency permutation jumps. This paper therefore proposes a new method based on two assumptions. The frequency continuity of the unmixing filters is still used in the initialization of the diagonalization algorithm. Then, the paper introduces a new method based on the time-frequency representations of the sources. They are assumed to vary smoothly with frequency. This hypothesis of the continuity of the time variation of the source energy is exploited on a sliding frequency bandwidth. It allows us to detect the remaining frequency permutation jumps. The method is compared with other approaches and results on real world recordings demonstrate superior performances of the proposed algorithm.
01 Oct 2006
TL;DR: A new macromodeling scheme for electrically long interconnects characterized by tabulated frequency responses, modeled as a superposition of multiple single-delay atoms, which are identified via a selective inversion of a Gabor transform of the raw frequency data.
Abstract: We introduce a new macromodeling scheme for electrically long interconnects characterized by tabulated frequency responses. The transfer function of the interconnect is modeled as a superposition of multiple single-delay atoms, which are identified via a selective inversion of a Gabor transform of the raw frequency data. Each atom is then approximated by a delayed rational function, leading to a highly-efficient SPICE-ready macromodel.
TL;DR: The authors propose and demonstrate the time-stretched short-time Fourier transform (TS-STFT) technique, which enhances the analog bandwidth and the effective sampling rate of the ADC and enables measurement of the instantaneous behavior of highly nonstationary ultrawideband signals.
Abstract: The authors propose and demonstrate the time-stretched short-time Fourier transform (TS-STFT) technique to overcome the limitation of an analog-digital converter (ADC) in the time-frequency analysis of ultrafast signals. Experimentally, the time-frequency analysis of highly chirped RF signals, with a chirp rate as high as 350 GHz/ns, is demonstrated. An effective real-time sampling rate of 320 GSa/s is achieved. Time stretching enhances the analog bandwidth and the effective sampling rate of the ADC and enables measurement of the instantaneous behavior of highly nonstationary ultrawideband signals.
TL;DR: In this paper, a Hilbert-Huang transform is proposed to analyze partial discharge data in electrical equipment, and its application in a PD application shows its great advantage, and the mathematical model of a Hilbert Huang transform is described.
Abstract: /sup T/he development of time-frequency and time-scale transforms has progressed rapidly over the last 20 years. The development and progression of numerical acquisition enable the utilization of these mathematical tools to analyze partial discharge in electrical equipment. This paper introduces a new transform, a Hilbert-Huang Transform to analyze partial discharge data. The mathematical model of a Hilbert-Huang Transform is described; and its application in a PD application shows its great advantage.
TL;DR: This paper proposes a processing method of marine-mammal signals, well adapted to a real passive underwater context, and tries to overcome the two major limitations of the warping operator principle.
Abstract: Processing marine-mammal signals for passive oceanic acoustic tomography or species classification and monitoring are problems that have recently attracted attention in scientific literature. For these purposes, it is necessary to use a method which could be able to extract the useful information about the processed data, knowing that the underwater environment is highly nonstationary. In this context, time-frequency (TF) or time-scale methods constitute a potential approach. Practically, it has been observed that the majority of TF structures of the marine-mammal signals are highly nonlinear. This fact affects dramatically the performances achieved by the Cohen's class methods, these methods being efficient in the presence of linear TF structures. Fortunately, thanks to the warping operator principle, it is possible to generate other class of time-frequency representations (TFRs). The new TFRs may analyze nonlinear chirp signals better than Cohen's class does. In spite of its mathematical elegance, this principle is limited in real applications by two major elements. First, as we will see, its implementation leads to a considerable growth of the signal length. Consequently, from operational point of view, this principle is limited to short synthetic signals. Second, the design of a single warping operator can be inappropriate if the analyzed signal is multicomponent. Furthermore, the choice of "adapted" warping operator becomes a problem when the signal components have different TF behaviors. In this paper, we propose a processing method of marine-mammal signals, well adapted to a real passive underwater context. The method tries to overcome the two aforementioned limitations. Also, the first step consists in data size reducing by the detection of the TF regions of interests (ROIs). Furthermore, in each ROI, a technique which combines some typical warping operators is used. The result is an analytical characterization of the instantaneous frequency laws (IFLs) of signal components. The simulations on real underwater data show the performances of this method in comparison with classical ones
01 Jan 2006
TL;DR: Using the extracted features of the dominant frequencies of the wavelet transform and the related scales, an artificial neural network (ANN) is trained and validated for SEMG classification.
Abstract: To date various signal processing techniques have been applied to surface electromyography (SEMG) for feature extraction and signal classification. Compared with traditional analysis methods which have been used in previous application, continuous wavelet transform (CWT) enhances the SEMG features more effectively. This paper presents methods of analysing SEMG signals using CWT and LabVIEW for extracting accurate patterns of the SEMG signals. We used the scalogram and frequency-time based spectrum to plot the power of the wavelet transform and enhance the diagnosis features of the signal. As a result, clinical interpretation of SEMG can be improved by extracting time-based information as well as scales, which can be converted to frequencies. Using the extracted features of the dominant frequencies of the wavelet transform and the related scales, we were able to train and validate an artificial neural network (ANN) for SEMG classification.
19 Jul 2006
TL;DR: In this paper, a framework for using time-frequency analysis methods for instantaneous system identification is discussed, where the Fourier transforms of successive portions of the record are assembled into a normalized timefrequency representation of the signal.
Abstract: Time-frequency analysis methods transform a time series into a two-dimensional representation
of frequency content with respect to time. The Fourier Transform identifies
the frequency content of a signal (as a sum of weighted sinusoidal functions) but
does not give useful information regarding changes in the character of the signal, as all
temporal information is encoded in the phase of the transform. A time-frequency representation,
by expressing frequency content at different sections of a record, allows
for analysis of evolving signals. The time-frequency transformation most commonly
encountered in seismology and civil engineering is a windowed Fourier Transform, or
spectrogram; by comparing the frequency content of the first portion of a record with
the last portion of the record, it is straightforward to identify the changes between
the two segments. Extending this concept to a sliding window gives the spectrogram,
where the Fourier transforms of successive portions of the record are assembled into a
time-frequency representation of the signal. The spectrogram is subject to an inherent
resolution limitation, in accordance with the uncertainty principle, that precludes
a perfect representation of instantaneous frequency content. The wavelet transform
was introduced to overcome some of the shortcomings of Fourier analysis, though
wavelet methods are themselves unsuitable for many commonly encountered signals.
The Wigner-Ville Distribution, and related refinements, represent a class of advanced
time-frequency analysis tools that are distinguished from Fourier and wavelet
methods by an increase in resolution in the time-frequency plane. I introduce several
time-frequency representations and apply them to various synthetic signals as well as
signals from instrumented buildings.
vi
For systems of interest to engineers, investigating the changing properties of a
system is typically performed by analyzing vibration data from the system, rather
than direct inspection of each component. Nonlinear elastic behavior in the forcedisplacement
relationship can decrease the apparent natural frequencies of the system
- these changes typically occur over fractions of a second in moderate to strong excitation
and the system gradually recovers to pre-event levels. Structures can also suffer
permanent damage (e.g., plastic deformation or fracture), permanently decreasing the
observed natural frequencies as the system loses stiffness. Advanced time-frequency
representations provide a set of exploratory tools for analyzing changing frequency
content in a signal, which can then be correlated with damage patterns in a structure.
Modern building instrumentation allows for an unprecedented investigation into
the changing dynamic properties of structures: a framework for using time-frequency
analysis methods for instantaneous system identification is discussed.
09 Jul 2006
TL;DR: This paper presents an algorithm for real-time iterative spectrogram inversion (RTISI) with look-ahead ( RTISI-LA), which reconstructs a time-domain signal from a given sequence of short-time Fourier transform magnitude (STFTM) spectra without phase information.
Abstract: In this paper, we present an algorithm for Real-time Iterative Spectrogram Inversion (RTISI) with Look-Ahead (RTISI-LA). RTISI-LA reconstructs a time-domain signal from a given sequence of short-time Fourier transform magnitude (STFTM) spectra without phase information. Whereas RTISI [1] reconstructs the current frame using only magnitude spectra information for previous frames and the current frame, RTISI-LA also uses magnitude spectra for a small number future frames. This allows RTISI-LA to achieve substantially higher signal-to-noise (SNR) performance than either RTISI or the Griffin & Lim method [2][3] with an equivalent computational load, while retaining the real-time properties of RTISI.
TL;DR: In the ISAR imaging, the use of HW-based TFR provides similar/better results with significant (92%) computational advantage compared to that obtained by CWD.
Abstract: A new approach has been proposed to reduce the computations involved in the ISAR imaging, which uses harmonic wavelet- (HW) based time-frequency representation (TFR). Since the HW-based TFR falls into a category of nonparametric time-frequency (T-F) analysis tool, it is computationally efficient compared to parametric T-F analysis tools such as adaptive joint time-frequency transform(AJTFT), adaptive wavelet transform(AWT), and evolutionary AWT (EAWT). Further, the performance of the proposed method of ISAR imaging is compared with the ISAR imaging by other nonparametric T-F analysis tools such as short-time Fourier transform(STFT) and Choi-Williams distribution (CWD). In the ISAR imaging, the use of HW-based TFR provides similar/better results with significant (92%) computational advantage compared to that obtained by CWD. The ISAR images thus obtained are identified using a neural network-based classification scheme with feature set invariant to translation, rotation, and scaling.
TL;DR: In this paper, wavelet and Gabor analyses for the modal parameter identification are compared and two reconstruction approaches - the FFT method and the Gabor expansion method$are also compared.
Abstract: Traditional modal-analysis methods use either time-domain or frequency-domain approaches. Because vibration signals are generally nonstationary, time and frequency information is needed simultaneously in many cases. This paper presents an overview of the applications of joint time-frequency methods for modal analysis. Since a joint time-frequency analysis can decouple vibration modes, it has an advantage, especially when information about the excitation is not available. In this paper, wavelet and Gabor analyses for the modal parameter identification are compared. Two reconstruction approaches - the FFT method and the Gabor expansion method$are also compared. Numerical simulations and experiments have been carried out
14 May 2006
TL;DR: An automatic segmentation algorithm of the real and imaginary parts of the STFT based on statistical features is proposed, which is an alternative to the spectrogram segmentations considered as image segmentations.
Abstract: Taking as signal model the sum of a non-stationnary deterministic part embedded in a white Gaussian noise, this paper presents the distribution of the coefficients of the Short Time Fourier Transform (STFT), which is used to determine the maximum likelihood estimator of the noise level. We then propose an automatic segmentation algorithm of the real and imaginary parts of the STFT based on statistical features, which is an alternative to the spectrogram segmentations considered as image segmentations. Examples of segmented time-frequency space are presented on a simulated signal and on a dolphin whistle.
TL;DR: A new linear time-frequency model in which the instantaneous value of each signal component is mapped to the curve functionally representing its instantaneous frequency, which is linear, uniquely defined by the signal decomposition, and satisfies linear marginal-like distribution properties.
Abstract: We describe a new linear time-frequency model in which the instantaneous value of each signal component is mapped to the curve functionally representing its instantaneous frequency. This transform is linear, uniquely defined by the signal decomposition, and satisfies linear marginal-like distribution properties. We further demonstrate the transform generated surface may be estimated from the short time Fourier transform by a concentration process based on the phase of the short-time Fourier transform (STFT), differentiated with respect to time. Interference may be identified on the concentrated STFT surface, and the signal with the interference removed may be estimated by applying the linear-time-marginal to the concentrated STFT surface from which the interference components have been removed
TL;DR: The usage of Matching Pursuit for time-frequency filtering of biomagnetic signals is proposed and its performance was tested for varying signal-to-noise ratios using both simulated and real MEG somatic evoked magnetic field data.
Abstract: Time-frequency signal analysis based on various decomposition techniques is widely used in biomedical applications. Matching Pursuit is a new adaptive approach for time-frequency decomposition of such biomedical signals. Its advantage is that it creates a concise signal approximation with the help of a small set of Gabor atoms chosen iteratively from a large and redundant set. In this paper, the usage of Matching Pursuit for time-frequency filtering of biomagnetic signals is proposed. The technique was validated on artificial signals and its performance was tested for varying signal-to-noise ratios using both simulated and real MEG somatic evoked magnetic field data.