Showing papers on "Time–frequency analysis published in 2005"
TL;DR: Methods based on the use of explicitly predefined signal features: the signal's amplitude envelope, spectral magnitudes and phases, time-frequency representations, and methods based on probabilistic signal models are discussed.
Abstract: Note onset detection and localization is useful in a number of analysis and indexing techniques for musical signals. The usual way to detect onsets is to look for "transient" regions in the signal, a notion that leads to many definitions: a sudden burst of energy, a change in the short-time spectrum of the signal or in the statistical properties, etc. The goal of this paper is to review, categorize, and compare some of the most commonly used techniques for onset detection, and to present possible enhancements. We discuss methods based on the use of explicitly predefined signal features: the signal's amplitude envelope, spectral magnitudes and phases, time-frequency representations; and methods based on probabilistic signal models: model-based change point detection, surprise signals, etc. Using a choice of test cases, we provide some guidelines for choosing the appropriate method for a given application.
802 citations
TL;DR: In this article, a semi-active variable stiffness tuned mass damper (SAIVS-TMD) was used to reduce the structural response of a wind excited tall building.
188 citations
TL;DR: A digital channelized receiver is presented for the interception of a wide variety of signals of complex structure, including those with low probability of interception, and shows a good performance in terms of detection, estimation, and processing of simultaneous signals.
Abstract: A digital channelized receiver is presented for the interception of a wide variety of signals of complex structure, including those with low probability of interception. The receiver is designed from the perspective of the time-frequency analysis. It uses an extended time-frequency representation based on the noncoherent integration of the short-time Fourier transform (STFT) on which the detection system and the encoder work. The encoder includes robust frequency estimation, automatic modulation recognition, and clustering, to handle broadband and simultaneous signals and to prevent out-of-channel detections (a typical phenomenon in channelized receivers). The receiver has been evaluated for a wide range of signals and shows a good performance in terms of detection, estimation, and processing of simultaneous signals. Signals collected from real-life systems and synthetic signals have been utilized.
122 citations
TL;DR: In this paper, a flexible approach for the time-frequency analysis of multicomponent signals involving the use of analytic vectors and demodulation is introduced, and the resulting instantaneous frequency of each component in each tile is not constrained to a set polynomial form in time, and is readily calculated, as is the corresponding Hilbert energy spectrum.
Abstract: In this paper, we introduce a flexible approach for the time-frequency analysis of multicomponent signals involving the use of analytic vectors and demodulation. The demodulated analytic signal is projected onto the time-frequency plane so that, as closely as possible, each component contributes exclusively to a different ‘tile’ in a wavelet packet tiling of the time-frequency plane, and at each time instant, the contribution to each tile definitely comes from no more than one component. A single reverse demodulation is then applied to all projected components. The resulting instantaneous frequency of each component in each tile is not constrained to a set polynomial form in time, and is readily calculated, as is the corresponding Hilbert energy spectrum. Two examples illustrate the method. In order better to understand the effect of additive noise, the approximate variance of the estimated instantaneous frequency in any tile has been formulated by starting with pure noise and studying its evolving covariance structure through each step of the algorithm. The validity and practical utility of the resulting expression for the variance of the estimated instantaneous frequency is demonstrated via a simulation experiment.
121 citations
TL;DR: The adaptive time- frequencies analysis method was applied to the analysis of dispersive elastic waves measured in waveguide experiments and a theoretical investigation on its time-frequency resolution was presented, and an iterative scheme for determining the relationships was developed.
Abstract: Although time-frequency analysis is effective for characterizing dispersive wave signals, the time-frequency tilings of most conventional analysis methods do not take into account dispersion phenomena. An adaptive time-frequency analysis method is introduced whose time-frequency tiling is determined with respect to the wave dispersion characteristics. In the dispersion-based time-frequency tiling, each time-frequency atom is adaptively rotated in the time-frequency plane, depending on the local wave dispersion. Although this idea can be useful in various problems, its application to the analysis of dispersive wave signals has not been made. In this work, the adaptive time-frequency method was applied to the analysis of dispersive elastic waves measured in waveguide experiments and a theoretical investigation on its time-frequency resolution was presented. The time-frequency resolution of the proposed transform was then compared with that of the standard short-time Fourier transform to show its effectiveness in dealing with dispersive wave signals. In addition, to facilitate the adaptive time-frequency analysis of experimentally measured signals whose dispersion relations are not known, an iterative scheme for determining the relationships was developed. The validity of the present approach in dealing with dispersive waves was verified experimentally.
77 citations
09 May 2005
TL;DR: The experiments show that Gabor features are robust in discriminating micro-Doppler effects of different types of micro-motions, and SVM classifier provides the best performance.
Abstract: In this paper, we propose a Gabor filtering method to extract localized micro-Doppler signatures represented in the time-frequency domain. The dimensionality of the extracted Gabor features is further reduced by using the principal component analysis (PCA) method. Therefore, a suitable classifier can be used for target classification based on their different motion dynamics. In our study, we use simulated radar data. Three different classifiers (Bayes linear, k-nearest neighbor, and support vector machine) are compared and tested. Our experiments show that Gabor features are robust in discriminating micro-Doppler effects of different types of micro-motions, and SVM classifier provides the best performance.
70 citations
TL;DR: In this article, various joint time-frequency distributions (TFDs) can optimize the trade-off between time resolution and frequency resolution for spectroscopic optical coherence tomography (SOCT) signals.
Abstract: The analysis of spectroscopic optical coherence tomography (SOCT) signals suffers the trade-off between time resolution and frequency resolution. Various joint time–frequency distributions (TFDs) can optimize this trade-off. Synthesized signals were generated and experimentally acquired data were obtained to compare and validate several different TFDs under different SOCT imaging schemes. Specific criteria were designed to quantify the TFD performance. We found that different SOCT imaging schemes require different optimal TFDs. Cohen’s class TFDs generate the most compact time–frequency (TF) analysis, while linear TFDs offer the most reliable TF analysis. In both cases, if some prior information is known, model-based TF analysis can improve the performance.
56 citations
TL;DR: Using the Gabor transform, a technique to correct reflection seismograms for the effects of anelastic attenuation and source signature is described and its effectiveness is illustrated and its superiority over the established Wiener deconvolution is demonstrated.
Abstract: Using the Gabor transform, we describe a technique to correct reflection seismograms for the effects of anelastic attenuation and source signature. Essentially we build a nonstationary deconvolution filter, estimated from the seismic data itself and applied by multiplication in the Gabor domain. In more detail, we estimate the time-frequency magnitude spectrum of the attenuation process and the source signature from the Gabor transform of a seismic signal; the phase then follows under the assumption of minimum phase. The deconvolution filter is the inverse of this estimate and is applied to the Gabor transform of the seismic signal by multiplication. An inverse Gabor transform completes the algorithm and gives a very high resolution estimate for the reflectivity of the earth.
As a justification for our algorithm we present a model for a seismic trace that uses a pseudodifferential operator to describe anelastic attenuation. We then argue that the Gabor transform approximately renders this pseudodifferential operator expression into a product of time-frequency dependent factors. Attenuation processes and source signature are removed by multiplication with estimates of their inverses.
With both real and synthetic data we illustrate the effectiveness of Gabor deconvolution and demonstrate its superiority over the established Wiener deconvolution.
54 citations
18 Mar 2005
TL;DR: A method to address the (inherently ill-posed) problem of missing data interpolation over repeated short gaps in audio signals by formulating the problem in terms of a Gabor regression model and showing that it is possible to leverage information from the surrounding time-frequency plane in order to obtain an interpolation in keeping with the qualities of the signal under consideration.
Abstract: We present a method to address the (inherently ill-posed) problem of missing data interpolation over repeated short gaps in audio signals. By formulating the problem in terms of a Gabor regression model, we show that it is possible to leverage information from the surrounding time-frequency plane in order to obtain an interpolation in keeping with the qualities of the signal under consideration. As an exploratory investigation of this technique's potential, we consider two example restoration scenarios in which over one third of the data values in total are missing.
52 citations
TL;DR: Two types of time–frequency (TF) blind source separation (BSS) methods suited to attenuated and delayed (AD) mixtures, especially suited to non-stationary sources are proposed and derive their performance from many tests performed with AD mixtures of speech signals.
52 citations
TL;DR: This paper uses the matching pursuits algorithm, which is a greedy adaptive decomposition, that has the potential of decomposing a signal into coherent components, to obtain coherent representations of electric power systems signals obtained by employing adaptive signal decompositions.
Abstract: This paper presents coherent representations of electric power systems signals. These representations are obtained by employing adaptive signal decompositions. They provide a tool to identify structures composing a signal and constitute an approach to represent a signal from its identified components. We use the matching pursuits algorithm, which is a greedy adaptive decomposition, that has the potential of decomposing a signal into coherent components. The dictionary employed is composed of damped sinusoids in order to obtain signal components closely related to power systems phenomena. In addition, we present an effective method to suppress the pre-echo and post-echo artifacts that often appear when using the matching pursuits. However, the use of a dictionary of damped sinusoids alone does not ensure that the decomposition will be meaningful in physical terms. To overcome this constraint, we develop a technique leading to efficient coherent damped-sinusoidal decompositions that are closely related to the physical phenomena being observed. The effectiveness of the proposed method for compression of synthetic and natural signals is tested, obtaining high compression ratios along with high signal-to-noise ratio.
TL;DR: A new design involving the latest technique in signal simulation was developed to create a controllable model of the electrocardiography signal, and the algorithm was shown to be advantageous in reducing ventilation artifacts and motion noise, resulting in good accuracy.
Abstract: In this paper the solution of the specialized measuring system for electrocardiography (ECG) signal recording and introductory recognition is presented. The project aims at designing the complete PC-based Virtual Instrument as a "testing platform" for acquisition, processing, presenting, and distributing ECG data. A new design involving the latest technique in signal simulation was developed to create a controllable model of the electrocardiography signal. Then it was implemented for testing of the developed QRS detection algorithm, based on the time-frequency analysis method. The processing stage involving discrete wavelet transform was used to detect QRS complexes in the ECG signal. By using the controlled signal model and the real ones, the algorithm was shown to be advantageous in reducing ventilation artifacts and motion noise, resulting in good accuracy.
TL;DR: In this article, the Fourier transform and the dual Gabor window of a Gabor frame are approximated by finite models in the Feichtinger space, which is dense in L2, much larger than the Schwartz space and a Banach space.
Abstract: Many results and problems in Fourier and Gabor analysis are formulated in the continuous variable case, i.e., for functions on ℝ. In contrast, a suitable setting for practical computations is the finite case, dealing with vectors of finite length. We establish fundamental results for the approximation of the continuous case by finite models, namely, the approximation of the Fourier transform and the approximation of the dual Gabor window of a Gabor frame. The appropriate function space for our approach is the Feichtinger space S0. It is dense in L2, much larger than the Schwartz space, and it is a Banach space.
TL;DR: Wavelet-based detection method shows better sensitivity than spectral and time-domain methods and effectiveness of the localization method in presence of complex power supply network, measurement noise, and process variation is addressed.
Abstract: Transient current (IDD) testing has been often cited and investigated as an alternative and/or supplement to quiescent current (IDDQ) testing. In this correspondence, we present a novel integrated method for fault detection and localization using wavelet transform-based IDD waveform analysis. The time-frequency resolution property of wavelet transform helps us detect as well as localize faults in digital CMOS circuits. Experiments performed on measured data from a fabricated 8-bit shift register, and simulation data from more complex circuits show promising results for both detection and localization. Wavelet-based detection method shows better sensitivity than spectral and time-domain methods. Effectiveness of the localization method in presence of complex power supply network, measurement noise, and process variation is also addressed.
TL;DR: Nonstationary jammers were considered and the local polynomial Fourier transform (LPFT) was used to represent the received corrupted signal, and procedure for an efficient optimization of the first-order LPFT is presented.
Abstract: Methods for jammer rejection in the spread spectrum communications, based on the time-frequency representations, have been proposed in order to improve the desired signal receiving performances. In this paper nonstationary jammers were considered and the local polynomial Fourier transform (LPFT) was used to represent the received corrupted signal. Time-varying filtering was implemented in optimal LPFT domain, having in mind that the LPFT is linear with respect to the signal. An order adaptive algorithm of the LPFT calculation is presented. Performance of the proposed nonparametric method is tested in the presence of linear and sinusoidal FM interferences in the noisy signal, without any a priori assumption about the jammer form. The proposed method may be successfully extended to the case of multiple jammers. Obtained results in terms of the bit error rate (BER) values show the achieved improvements. Procedure for an efficient optimization of the first-order LPFT is presented.
TL;DR: Using scalp EEG and subdural ECoG example datasets, parametric tests are evaluated as a replacement for previously applied computer-intensive resampling methods and the performance of different estimates of energy density is evaluated.
TL;DR: A new digital signal-processing method for instantaneous frequency estimation that assures superior accuracy and resolving capability with respect to other solutions already available in the literature, thus showing itself very attractive in the presence of multicomponent signals characterized by instantaneous frequency trajectories extremely similar and very close to one another.
Abstract: A new digital signal processing method for instantaneous frequency estimation is proposed here. The attention is mainly paid to signals whose instantaneous frequency trajectories exhibit a periodic evolution versus time. Thanks to an optimized use of the warblet transform, the method assures superior accuracy and resolving capability with respect to other solutions already available in the literature, thus showing itself very attractive in the presence of multicomponent signals characterized by instantaneous frequency trajectories extremely similar and very close to one another. Theoretical notes regarding the warblet transform and its optimized use in the framework of the proposed method are first given. Then, the fundamental steps of the method are described in detail with references to a clarifying example. The results of a number of experiments on emulated and actual signals, aimed at assessing the performance of the method, are finally presented.
TL;DR: In this paper, the authors prove the boundedness of a general class of Fourier multipliers, in particular of the Hilbert transform, on modulation spaces, using Gabor frames and methods from time-frequency analysis.
18 Mar 2005
TL;DR: A modified version of the spectrogram is presented which incorporates the centered discrete fractional Fourier transform and its multiangle version and which instead decomposes the signal over the analysis frame into multiple chirp signals.
Abstract: The spectrogram is a useful tool for the time-frequency analysis of non stationary signals. This tool, however, is based upon a multicomponent sinusoidal model over a signal analysis frame and is not suitable when, for example, the frequency content over the frame is chirping. Recently, the centered version of the discrete fractional Fourier transform was shown to possess the capability to concentrate a linear chirp signal in a few transform coefficients. We present a modified version of the spectrogram which incorporates the centered discrete fractional Fourier transform and its multiangle version and which instead decomposes the signal over the analysis frame into multiple chirp signals. We present simulation results that study the efficiency of this improved spectrogram and its application to the analysis of harmonically related chirps and bat echolocation signals.
TL;DR: The range of shape factor of the Gabor wavelet is examined in the analysis of spectral phase retrieval with an interferogram and it is demonstrated that for the pulses with moderate high order phase the accuracy of the retrieved phase is insensitive to the shaping factor.
Abstract: We examined the range of shape factor of the Gabor wavelet in the analysis of spectral phase retrieval with an interferogram. We demonstrated that for the pulses with moderate high order phase the accuracy of the retrieved phase is insensitive to the shaping factor in the range of 1∼20. Both simulated ideal Gaussian spectrum and actual non-Gaussian spectrum are applied in the analysis.
18 Mar 2005
TL;DR: A new method for the extraction of spectral trajectories from nonstationary multicomponent narrowband signals by applying statistical filtering so as to track frequency trajectories in the short term Fourier transform is investigated.
Abstract: The paper investigates a new method for the extraction of spectral trajectories from nonstationary multicomponent narrowband signals. The main idea is to apply statistical filtering so as to track frequency trajectories in the short term Fourier transform. A nonlinear observation model is defined and a special particle filtering algorithm is developed.
18 Mar 2005
TL;DR: The results obtained for real data prove the capability of the proposed approach for accurately detect and characterize signals with a complex TF behavior.
Abstract: The problem of signal detection, followed by a characterization stage, is considered in this paper. The main difficulties arising in the detection stage are caused by noise, which acts in a real environment, and by multiple time-frequency (TF) structures of the signal. In this paper a detection method based on the adaptive grouping of the TF information provided by a Gabor filter bank is proposed. A Viterbi-type algorithm is used as a tool for grouping of TF components. The results obtained for real data prove the capability of the proposed approach for accurately detect and characterize signals with a complex TF behavior.
TL;DR: Numerical simulations with synthesized multicomponent signals show that the proposed algorithms achieve better performance on instantaneous frequency estimation with greatly reduced computational complexity.
Abstract: This paper presents efficient algorithms for the analysis of nonstationary multicomponent signals based on modified local polynomial time-frequency transform. The signals to be analyzed are divided into a number of segments and the desired parameters for computing the modified local polynomial time-frequency transform in each segment are estimated from polynomial Fourier transform in the frequency domain. Compared to other reported algorithms, the length of overlap between consecutive segments is reduced to minimize the overall computational complexity. The concept of adaptive window lengths is also employed to achieve a better time-frequency resolution for each component. Numerical simulations with synthesized multicomponent signals show that the proposed ones achieve better performance on instantaneous frequency estimation with greatly reduced computational complexity.
09 May 2005
TL;DR: This paper presents several implementations of digital channelised receivers on field-programmable gate array (FPGA) platforms for electronic warfare (EW) applications based on the fast Fourier transform (FFT), designed to maximise speed processing and throughput, and to optimise area.
Abstract: This paper presents several implementations of digital channelised receivers on field-programmable gate array (FPGA) platforms for electronic warfare (EW) applications. All implementations are based on the fast Fourier transform (FFT) but they are intended for different applications. We have studied in detail and implemented different parallel architectures for the FFT algorithm in order to maximise speed processing and throughput, and to optimise area. On the other hand, monobit implementations of the FFT have been carried out in order to get real time in broadband digital receivers. Finally, in order to improve the detection of non-stationary signals, time-frequency analysis based on the short time Fourier transform (STFT) has also been implemented.
18 Sep 2005
TL;DR: It has been shown that by using both simulated chirp signals and ultrasonic experimental data, the chirplet signal decomposition algorithm performs robustly, yields accurate echo estimation and results in SNR enhancements.
Abstract: In this investigation, the chirplet transform is introduced as a means to obtain not only time-frequency representation, but also to estimate the echo amplitude, time of arrival, center frequency, bandwidth, phase, and chirp rate of multiple interfering ultrasonic echoes. This transformation can be used for signal decomposition and successive parameter estimation of multiple interfering echoes. It has been shown that by using both simulated chirp signals and the ultrasonic experimental data, 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 precise signal representation. This type of study addresses a broad range of applications including flaw detection, deconvolution, object classification, velocity measurement, and ranging systems.
TL;DR: In this method, a comparison between tonotopic and instantaneous frequency information is introduced to select filter positions that are well matched to the signal, and a cross-check that compares frequency estimates from neighboring channels is used to optimize filter bandwidth, and to signal the quality of the analysis.
Abstract: Classical time–frequency analysis is based on the amplitude responses of bandpass filters, discarding phase information. Instantaneous frequency analysis, in contrast, is based on the derivatives of these phases. This method of frequency calculation is of interest for its high precision and for reasons of similarity to cochlear encoding of sound. This article describes a methodology for high resolution analysis of sparse sounds, based on instantaneous frequencies. In this method, a comparison between tonotopic and instantaneous frequency information is introduced to select filter positions that are well matched to the signal. Second, a cross-check that compares frequency estimates from neighboring channels is used to optimize filter bandwidth, and to signal the quality of the analysis. These cross-checks lead to an optimal time–frequency representation without requiring any prior information about the signal. When applied to a signal that is sufficiently sparse, the method decomposes the signal into separate time–frequency contours that are tracked with high precision. Alternatively, if the signal is spectrally too dense, neighboring channels generate inconsistent estimates—a feature that allows the method to assess its own validity in particular contexts. Similar optimization principles may be present in cochlear encoding.
06 Dec 2005
TL;DR: In this paper, the use of auditory-based representations instead of the short time Fourier transform (STFT) was evaluated for the case of speech and music signals, and it was shown that auditory representations based on the equal rectangular bandwidth (ERB) and Bark frequency scales can improve the disjointness of the transformed mixtures.
Abstract: Sparsity-based source separation algorithms often rely on a transformation into a sparse domain to improve mixture disjointness and therefore facilitate separation To this end, the most commonly used time-frequency representation has been the short time Fourier transform (STFT) The purpose of this paper is to study the use of auditory-based representations instead of the STFT We first evaluate the STFT disjointness properties for the case of speech and music signals, and show that auditory representations based on the equal rectangular bandwidth (ERB) and Bark frequency scales can improve the disjointness of the transformed mixtures
TL;DR: It is suggested that TFCA may serve as an appropriate tool for capturing the localized ERP components in the time-frequency domain and for studying the intricate, frequency-based dynamics of the human brain.
TL;DR: The newly developed EMD scheme has the highest potential in separating the modal components and by application of the EMD both instantaneous amplitude and frequency can be accurately determined.
TL;DR: Theoretical and simulation experimental analyses show that the oversampled Gabor transform using the Gaussian synthesis window is more suitable for the NMR FID signal enhancement than the critically-sampled one using the exponential synthesis window.
Abstract: An efficient algorithm to reduce the noise from the Nuclear Magnetic Resonance Free Induction Decay (NMR FID) signals is presented, in this paper, via the oversampled real-valued discrete Gabor transform using the Gaussian synthesis window. An NMR FID signal in the Gabor transform domain (i.e., a joint time-frequency domain) is concentrated in a few number of Gabor transform coefficients while the noise is fairly distributed among all the coefficients. Therefore, the NMR FID signal can be significantly enhanced by performing a thresholding technique on the coefficients in the transform domain. Theoretical and simulation experimental analyses in this paper show that the oversampled Gabor transform using the Gaussian synthesis window is more suitable for the NMR FID signal enhancement than the critically-sampled one using the exponential synthesis window, because both the Gaussian synthesis window and its corresponding analysis window in the oversampling case can have better localization in the frequency domain than the exponential synthesis window and its corresponding analysis window in the critically-sampling case. Moreover, to speed up the transform, instead of the commonly-used complex-valued discrete Gabor transform, the real-valued discrete Gabor transform presented in our previous work is adopted in the proposed algorithm.