Showing papers on "Time–frequency analysis published in 2018"
TL;DR: In this paper, a time-frequency analysis method based on ensemble local mean decomposition (ELMD) and fast kurtogram (FK) is proposed for rotating machinery fault diagnosis.
182 citations
TL;DR: In this paper, a matching synchrosqueezing transform (MSST) was proposed to improve the readability of the TF representation of nonstationary signals composed of multiple components with slow varying instantaneous frequency (IF).
141 citations
TL;DR: The experimental results indicate that the proposed MRBF-MPSO-SVM classification method outperforms competing techniques in terms of classification accuracy, and shows the effectiveness of the proposed method for classification of seizure epochs and seizure-free epochs.
Abstract: The automatic detection of epileptic seizures from electroencephalography (EEG) signals is crucial for the localization and classification of epileptic seizure activity. However, seizure processes are typically dynamic and nonstationary, and thus, distinguishing rhythmic discharges from nonstationary processes is one of the challenging problems. In this paper, an adaptive and localized time–frequency representation in EEG signals is proposed by means of multiscale radial basis functions (MRBF) and a modified particle swarm optimization (MPSO) to improve both time and frequency resolution simultaneously, which is a novel MRBF-MPSO framework of the time–frequency feature extraction for epileptic EEG signals. The dimensionality of extracted features can be greatly reduced by the principle component analysis algorithm before the most discriminative features selected are fed into a support vector machine (SVM) classifier with the radial basis function (RBF) in order to separate epileptic seizure from seizure-free EEG signals. The classification performance of the proposed method has been evaluated by using several state-of-art feature extraction algorithms and other five different classifiers like linear discriminant analysis, and logistic regression. The experimental results indicate that the proposed MRBF-MPSO-SVM classification method outperforms competing techniques in terms of classification accuracy, and shows the effectiveness of the proposed method for classification of seizure epochs and seizure-free epochs.
125 citations
TL;DR: The proposed TFR based on the improved eigenvalue decomposition of Hankel matrix and Hilbert transform has achieved classification accuracy 100% for the studied EEG database and gives good performance in terms of Renyi entropy measure.
Abstract: Time–frequency representation (TFR) is useful for non-stationary signal analysis as it provides information about the time-varying frequency components. This study proposes a novel TFR based on the improved eigenvalue decomposition of Hankel matrix and Hilbert transform (IEVDHM–HT). In the proposed method, first the authors decompose non-stationary signals using the IEVDHM with suitably defined criterion for eigenvalue selection, requirement of number of iterations, and new component merging criteria. Furthermore, the HT is applied on extracted components in order to obtain the TFR of non-stationary signals. The performance of proposed TFR has been evaluated on synthetic signals in clean and white noise environment with different signal-to-noise ratios. The proposed method gives good performance in terms of Renyi entropy measure in comparison with other existing methods. Application of the proposed TFR is also shown for the classification of epileptic seizure electroencephalogram (EEG) signals. The least-square support vector machine (LS-SVM) with radial basis function kernel is used for classification of seizure and seizure-free EEG signals obtained from the publicly available database by the University of Bonn, Germany. The proposed method has achieved classification accuracy 100% for the studied EEG database.
115 citations
TL;DR: This paper is to present alternative formulations of Morlet wavelets in time and in frequency that allow parameterizing the wavelets directly in terms of the desired temporal and spectral smoothing (as full-width at half-maximum).
Abstract: Morlet wavelets are frequently used for time-frequency analysis of non-stationary time series data, such as neuroelectrical signals recorded from the brain. The crucial parameter of Morlet wavelets is the width of the Gaussian that tapers the sine wave. This width parameter controls the trade-off between temporal precision and frequency precision. It is typically defined as the "number of cycles," but this parameter is opaque, and often leads to uncertainty and suboptimal analysis choices, as well as being difficult to interpret and evaluate. The purpose of this paper is to present alternative formulations of Morlet wavelets in time and in frequency that allow parameterizing the wavelets directly in terms of the desired temporal and spectral smoothing (as full-width at half-maximum). This formulation provides clarity on an important data analysis parameter, and should facilitate proper analyses, reporting, and interpretation of results. MATLAB code is provided.
111 citations
TL;DR: New compound fault features, extracted from continuous and discrete wavelet transform of vibration signal are proposed and fault classification accuracy of these features is found to be better than the conventional time and frequency domain parameters.
104 citations
TL;DR: A whale optimization algorithm (WOA)-optimized orthogonal matching pursuit (OMP) with a combined time–frequency atom dictionary with comparisons with the state of the art in the field are illustrated in detail, which highlight the advantages of the proposed method.
91 citations
TL;DR: In this paper, a bilinear time-frequency (TF) kernel is designed to suppress cross-terms and artifacts due to missing observations while preserving the FH signal autoterms, and the kerneled results are represented in the instantaneous autocorrelation function domain, which are then processed using a redesigned structure-aware Bayesian compressive sensing algorithm.
Abstract: In this paper, we address the problem of spectrum estimation of multiple frequency-hopping (FH) signals in the presence of random missing observations. The signals are analyzed within the bilinear time–frequency (TF) representation framework, where a TF kernel is designed by exploiting the inherent FH signal structures. The designed kernel permits effective suppression of cross-terms and artifacts due to missing observations while preserving the FH signal autoterms. The kerneled results are represented in the instantaneous autocorrelation function domain, which are then processed using a redesigned structure-aware Bayesian compressive sensing algorithm to accurately estimate the FH signal TF spectrum. The proposed method achieves high-resolution FH signal spectrum estimation even when a large portion of data observations is missing. Simulation results verify the effectiveness of the proposed method and its superiority over existing techniques.
87 citations
TL;DR: The concept of cepstrum is applied to eliminate the wave-shape function influence on the TF analysis, and a new algorithm, named de-shape synchrosqueezing transform (de-shape SST), is proposed.
Abstract: We propose to combine cepstrum and nonlinear time–frequency (TF) analysis to study multiple component oscillatory signals with time-varying frequency and amplitude and with time-varying non-sinusoidal oscillatory pattern. The concept of cepstrum is applied to eliminate the wave-shape function influence on the TF analysis, and we propose a new algorithm, named de-shape synchrosqueezing transform (de-shape SST). The mathematical model, adaptive non-harmonic model, is introduced and the de-shape SST algorithm is theoretically analyzed. In addition to simulated signals, several different physiological, musical and biological signals are analyzed to illustrate the proposed algorithm.
76 citations
TL;DR: Synthetic data examples show that this TPST achieves an optimized TF resolution, compared with the standard ST and modified ST with two parameters, and field data experiments illustrate that the TPST is superior to the ST in highlighting the channel edges.
Abstract: The S transform (ST) is one of the most commonly used time–frequency (TF) analysis algorithms and is commonly used in assisting reservoir characterization and hydrocarbon detection. Unfortunately, the TF spectrum obtained by the ST has a low temporal resolution at low frequencies, which lowers its ability in thin beds and channels detection. In this letter, we propose a three parameters ST (TPST) to optimize the TF resolution flexibly. To demonstrate the validity and effectiveness of the TPST, we first apply it to a synthetic data and a synthetic seismic trace and then to a filed data. Synthetic data examples show that this TPST achieves an optimized TF resolution, compared with the standard ST and modified ST with two parameters. Field data experiments illustrate that the TPST is superior to the ST in highlighting the channel edges. The lateral continuity of the frequency slice produced by the TPST is more continuous than that of the ST.
73 citations
TL;DR: In this paper, a gear fault diagnosis method based on structured sparsity time-frequency analysis (SSTFA) is proposed, which utilizes mixed-norm priors on timefrequency coefficients to obtain a fine match for the structure of signals.
TL;DR: A novel sparse time-frequency representation (STFR) method is proposed to increase the diagnostic precision of incipient faults and comparison results with state-of-the-art methods illustrate the superiority and robustness of the proposed method in the engineering applications.
Abstract: As wind power attracts increasing attention and wind turbines (WTs) capacity expands, fault diagnosis of WT is playing a more and more important role in improving reliability, minimizing down time, reducing maintenance costs, and providing reliable power generation. In this paper, a novel sparse time-frequency representation (STFR) method is proposed to increase the diagnostic precision of incipient faults. The proposed method can be applied once the condition is detected as abnormal according to the VDI3834 vibration threshold standard in WT fault diagnosis systems. The proposed method is a novel signal representation method based on the sparse representation theory and Wigner–Ville distribution (WVD), which can overcome the limitations of traditional basis functions expansion and time-frequency analysis methods. In this method, a union of redundant dictionary (URD) is constructed on the basis of the underlying prior information of the oscillate characteristics with multicomponent coupling effect and different morphological waveforms. Therefore, the vibration signal can be sparsely represented over the URD. Then, the sparse coefficients and corresponding atoms can be obtained by solving the basis pursuit denoising problem via alternating direction method of multipliers. Based on the combination of the WVD of each atom and corresponding sparse coefficient, the time-frequency distribution of the vibration signal can be obtained. To verify the effectiveness of the STFR method, a simulation and two field tests in the wind farm are performed. The comparison results with state-of-the-art methods illustrate the superiority and robustness of the proposed method in the engineering applications.
TL;DR: The motivation behind this paper is to overcome the potential low performance of empirical mode decomposition (EMD) for energy preservation of the steeply dipping events when used for noise attenuation, and low resolution when using for signal decomposition.
Abstract: We have introduced a new decomposition method for seismic data, termed complex variational mode decomposition (VMD), and we have also designed a new filtering technique for random noise attenuation in seismic data by applying the VMD on constant-frequency slices in the frequency–offset ( $f$ – $x$ ) domain. The motivation behind this paper is to overcome the potential low performance of empirical mode decomposition (EMD) for energy preservation of the steeply dipping events when used for noise attenuation, and low resolution when used for signal decomposition. The VMD is proposed to decompose a signal into an ensemble of band-limited modes. For seismic data consisting of linear events, the constant-frequency slices of its $f$ – $x$ spectrum are exactly band-limited. The noise attenuation algorithm is summarized as follows. First, the Fourier transform is applied on the time axis of the 2-D seismic data. Next, the VMD is applied on each frequency slice of the $f$ – $x$ spectrum and the decomposed modes are combined to obtain the filtered frequency slice. Finally, an inverse Fourier transform is applied on the frequency axis of the $f$ – $x$ spectrum to obtain the denoised result. The resulting VMD-based noise attenuation method is equivalent to applying a Wiener filter on each decomposed mode, which is achieved during the decomposition progress. We also applied 2-D VMD on 3-D seismic data for denoising. Numerical results show that the proposed VMD-based method achieves a higher denoising quality than both the $f$ – $x$ deconvolution method and the EMD-based denoising method, especially for preserving the steep slopes.
TL;DR: Experiments show that after using this implement of time-frequency analysis and convolutional neural network to process 4000 vibration signal samples generated by four different vibration events, the recognition rates of vibration events are over 90%.
Abstract: Based on vibration signals detected by a phase-sensitive optical time-domain reflectometer distributed optical fiber sensing system, this paper presents an implement of time-frequency analysis and convolutional neural network (CNN), used to classify different types of vibrational events. First, spectral subtraction and the short-time Fourier transform are used to enhance time-frequency features of vibration signals and transform different types of vibration signals into spectrograms, which are input to the CNN for automatic feature extraction and classification. Finally, by replacing the soft-max layer in the CNN with a multiclass support vector machine, the performance of the classifier is enhanced. Experiments show that after using this method to process 4000 vibration signal samples generated by four different vibration events, namely, digging, walking, vehicles passing, and damaging, the recognition rates of vibration events are over 90%. The experimental results prove that this method can automatically make an effective feature selection and greatly improve the classification accuracy of vibrational events in distributed optical fiber sensing systems.
TL;DR: An adaptive iterative generalized demodulation (AIGD) is proposed, which is highly adaptive to reveal the frequency contents and track their time variability of a signal, and provides an effective approach to nonstationary complex multi-component signal analysis.
TL;DR: An algorithm for synchrophasor, fundamental frequency, and rate of change of frequency estimation tailored for processing platforms with limited computational resources is described and characterized extensively in terms of both accuracy and processing time.
Abstract: Nowadays, the interest in low-cost and increasingly accurate phasor measurement units (PMUs) for active distribution systems is steadily growing. In this paper, an algorithm for synchrophasor, fundamental frequency, and rate of change of frequency estimation tailored for processing platforms with limited computational resources is described and characterized extensively in terms of both accuracy and processing time. The proposed solution harnesses the main advantages of two state-of-the-art algorithms, i.e., the interpolated discrete Fourier transform and the Taylor–Fourier transform. Such algorithms are combined and implemented in a computationally efficient manner to reduce processing time as much as possible, while ensuring good accuracy in the main testing conditions specified in the IEEE Standard C37.118.1-2011 and its Amendment C37.118.1a-2014. Estimation accuracy has been evaluated not only through simulations but also experimentally. The good consistency between simulation-based and experimental results provides clear evidence that the uncertainty contributions due to transducers, acquisition, and synchronization systems can be reasonably kept under control. The processing times of the algorithm, implemented on an embedded platform suitable for PMU prototyping, are compliant with the mandatory reporting rates of Class M PMUs.
TL;DR: The proposed synchrosqueezing generalized S-transform belongs to a postprocessing procedure of the GST and achieves a high resolution and has the potential in highlighting geological structures with high precision.
Abstract: In this letter, a new method is introduced for a seismic time–frequency (TF) analysis. The proposed method is called synchrosqueezing generalized S-transform (SSGST), which belongs to a postprocessing procedure of the GST. The frequency-dependent Gaussian window used in the standard S-transform may be not suitable for real applications. In order to overcome this limitation, the frequency-dependent Gaussian window is replaced by a parameterized function containing three parameters. These three parameters result in flexibility in the variation of TF resolution. Then, the synchrosqueezing transform is employed to squeeze the TF coefficients of the GST to achieve an energy-concentrated TF representation. Synthetic examples and field data show that the SSGST achieves a high resolution and has the potential in highlighting geological structures with high precision.
TL;DR: A differential protection scheme, based on real-time analysis of a time–frequency based technique, is proposed for a transmission line with a midpoint-connected static synchronous compensator (STATCOM).
Abstract: A differential protection scheme, based on real-time analysis of a time–frequency based technique, is proposed for a transmission line with a midpoint-connected static synchronous compensator (STATCOM). The first intrinsic mode function obtained from the decomposition of current signals from both ends of the transmission system, processed through an ensemble empirical mode decomposition technique, is used to estimate the discrete Teager energy (DTE) through online Hilbert–Huang transformation. The differential DTE from both ends of the line is used to detect the exact faulty phase. For analysis, a midpoint STATCOM compensated transmission line model is considered and simulated using EMTDC/PSCAD. Test cases, such as high fault resistance, fault inception angle, reverse power flow, current transformer saturation, and variation in source impedance, are generated for different operating modes of the STATCOM. The method is also tested for a cross-country fault in a double-circuit transmission system. Comparative assessment reports with other conventional approaches verify the reliability, speed, and feasibility of the proposed method.
TL;DR: Analytical formulations, approximations, upper and lower bounds for the angle sweep of maximum magnitude of fractional Fourier transform of mono- and multicomponent linear frequency modulated (LFM) signals are proposed.
Abstract: We propose analytical formulations, approximations, upper and lower bounds for the angle sweep of maximum magnitude of fractional Fourier transform of mono- and multicomponent linear frequency modulated (LFM) signals. We employ a successive coarse-to-fine grid-search algorithm to estimate the chirp rates of multicomponent nonseparable LFM signals. Extensive numerical simulations show the validity of analytical formulations and performance of the proposed estimator. Obtained analytical results may also find themselves other application areas, where nonstationary signals are of interest.
TL;DR: A novel iterative source separation procedure is proposed that outperforms the state-of-the-art consistent Wiener filter in minimizing the mixing error by means of the auxiliary function method.
Abstract: For audio source separation applications, it is common to estimate the magnitude of the short-time Fourier transform (STFT) of each source. In order to further synthesize time-domain signals, it is necessary to recover the phase of the corresponding complex-valued STFT. Most authors in this field choose a Wiener-like filtering approach, which boils down to use the phase of the original mixture. In this paper, a different standpoint is adopted. Many music events are partially composed of slowly varying sinusoids and the STFT phase increment over time of those frequency components takes a specific form. This allows phase recovery by an unwrapping technique once a short-term frequency estimate has been obtained. Herein, a novel iterative source separation procedure is proposed that builds upon these results. It consists in minimizing the mixing error by means of the auxiliary function method. This procedure is initialized by exploiting the unwrapping technique in order to generate estimates that benefit from a temporal continuity property. Experiments conducted on realistic music pieces show that, given accurate magnitude estimates, this procedure outperforms the state-of-the-art consistent Wiener filter.
TL;DR: An effective quasi maximum likelihood – random samples consensus algorithm is proposed for the instantaneous frequency (IF) estimation of overlapping signals in the time-frequency (TF) plane.
Abstract: Effective quasi maximum likelihood – random samples consensus algorithm is proposed for the instantaneous frequency (IF) estimation of overlapping signals in the time-frequency (TF) plane. The proposed algorithm is tested on signal with five overlapping components. The IF estimation accuracy is excellent with signal-to-noise ratio threshold below 0 dB.
TL;DR: A novel approach based on Hough transform is proposed to identify the clusters and channel characteristics in composite, intracluster, and time-variant levels are analyzed, and parameters investigated include the composite root-mean-square (RMS) delay spreads and power decay versus delay behaviors of clusters and clutter paths.
Abstract: In this paper, a recently conducted measurement campaign for vehicle-to-vehicle (V2V) propagation channel characterization is introduced. Two vehicles carrying a transmitter and a receiver, respectively, have been driven along an eight-lane road with heavy traffic. The measurement was conducted with 100 MHz signal bandwidth at a carrier frequency of 5.9 GHz. Channels are observed consisting of two kinds of channel components, i.e., time-evolving clusters and clutter paths. A novel approach based on Hough transform is proposed to identify the clusters. Based on the cluster identification results, channel characteristics in composite, intracluster, and time-variant levels are analyzed. The parameters investigated include the composite root-mean-square (RMS) delay spreads and power decay versus delay behaviors of clusters and clutter paths, cluster RMS delay spread, cluster RMS Doppler frequency spread, correlations of cluster parameters, and coherence time of parameters of interest. The statistics constitute an empirical stochastic clustered-delay-line channel model focusing on the wideband characteristics observed in the realistic time-variant V2V propagation scenario.
TL;DR: A new single-ended travelling-wave-based protection algorithm using the 2 ms full-waveform post-fault signal in the time–frequency domain, which avoids the discrimination of the reflected wavefront is presented.
Abstract: Single-ended travelling-wave-based protection has been available for several years, providing the advantages of low cost and no requirement for communication and synchronisation with the remote end. However, conventional single-ended travelling-wave-based protection has low reliability due to its use of only partial fault information, and it has high dependence on extracting a second reflected wavefront. A new single-ended travelling-wave-based protection algorithm using the 2 ms full-waveform post-fault signal in the time–frequency domain, which avoids the discrimination of the reflected wavefront, is presented. First, the full-waveform representation of the travelling wave in 3D subspace is proposed. The fault characteristics of the wideband travelling wave are analysed at different scales. Second, for faults that occur within one line or on adjacent lines, the propagation characteristics and reflection and refraction process are analysed in detail. Then, the correlation and difference of the full waveform are presented qualitatively and quantitatively. Finally, a time–frequency spectrum matrix is established based on the full waveform, and the protection algorithm is developed by using a time–frequency spectrum matrix and wave matching technology. Extensive simulations under different conditions verify the wide applicability and high reliability of the proposed algorithm.
TL;DR: This work proposes an EMD-based algorithm assisted by sinusoidal functions with a designed uniform phase distribution with a comprehensive theoretical explanation for the substantial reduction of the mode splitting and the residual noise effects simultaneously.
Abstract: The empirical mode decomposition (EMD) is an established method for the time–frequency analysis of nonlinear and nonstationary signals. However, one major drawback of the EMD is the mode mixing effect. Many modifications have been made to resolve the mode mixing effect. In particular, disturbance-assisted EMDs, such as the noise-assisted EMD and the masking EMD, have been proposed to resolve this problem. These disturbance-assisted approaches have led to a better performance of the EMD in the analysis of real-world data sets, but they may also have two side effects: the mode splitting and residual noise effects. To minimize or eliminate the mode mixing effect while avoiding the two side effects of traditional disturbance-assisted EMDs, we propose an EMD-based algorithm assisted by sinusoidal functions with a designed uniform phase distribution with a comprehensive theoretical explanation for the substantial reduction of the mode splitting and the residual noise effects simultaneously. We examine the performance of the new method and compare it to those of other disturbance-assisted EMDs using synthetic signals. Finally numerical experiments with real-world examples are conducted to verify the performance of the proposed method.
TL;DR: A novel technique is presented based on the high-order synchrosqueezing transform, which obtains more accurate instantaneous frequencies by using the higher order approximations for both amplitude and phase in order to achieve a highly energy-concentrated time-frequency representation.
Abstract: Time-frequency analysis always plays a central role in the field of seismic processing due to the advantage in characterizing nonstationary signals. In this letter, we present a novel technique for seismic time-frequency analysis based on the high-order synchrosqueezing transform, which obtains more accurate instantaneous frequencies by using the higher order approximations for both amplitude and phase in order to achieve a highly energy-concentrated time-frequency representation. A synthetic example is employed to demonstrate the validity of the proposed method in sharpening time-frequency representation. Application on field data example further proves its potential in enhancing time-frequency resolution and delineating stratigraphic characteristics with higher precision and renders that this technique is promising for seismic data analysis.
TL;DR: The milestone developments of time–frequency analysis in the past few decades are reviewed and the fundamental principles and structural engineering applications of wavelet analysis and Hilbert transform analysis in system identification, damage detection, and nonlinear modeling are summarized.
Abstract: Nonlinear dynamic behaviors of civil engineering structures have been observed not only under extreme loads but also during normal operations. Characterization of the time-varying property or nonlinearity of the structures must account for temporal evolution of the frequency and amplitude contents of nonstationary vibration responses. Neither time analysis nor frequency analysis method alone can fully describe the nonstationary characteristics. In this article, an attempt is made to review the milestone developments of time–frequency analysis in the past few decades and summarize the fundamental principles and structural engineering applications of wavelet analysis and Hilbert transform analysis in system identification, damage detection, and nonlinear modeling. This article is concluded with a brief discussion on challenges and future research directions with the application of time–frequency analysis in structural engineering.
TL;DR: The proposed method can correctly compute the spectrum of the input signal, and could be used in seismic data analysis to facilitate interpretation and capture the philosophy of the empirical mode decomposition.
Abstract: The empirical mode decomposition aims to decompose the input signal into a small number of components named intrinsic mode functions with slowly varying amplitudes and frequencies. In spite of its simplicity and usefulness, however, the empirical mode decomposition lack solid mathematical foundation. In this paper, we describe a method to extract the intrinsic mode functions of the input signal using non-stationary Prony method. The proposed method captures the philosophy of the empirical mode decomposition, but use a different method to compute the intrinsic mode functions. Having the intrinsic mode functions obtained, we then compute the spectrum of the input signal using Hilbert transform. Synthetic and field data validate the proposed method can correctly compute the spectrum of the input signal, and could be used in seismic data analysis to facilitate interpretation.
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TL;DR: In this article, the authors proposed a time-frequency analysis method based on the adaptive harmonic model and applied FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) to achieve an efficient numerical approximation of the functional.
TL;DR: In this paper, a comparison of a variety of approaches to estimating and postprocessing features is presented, and a variant of a standard filter bank-based approach, coupled with first and second derivatives, provides a substantial reduction in the overall error rate and improves the ability to discriminate between signal events and background noise.
Abstract: Feature extraction for automatic classification of EEG signals typically relies on time frequency representations of the signal. Techniques such as cepstral-based filter banks or wavelets are popular analysis techniques in many signal processing applications including EEG classification. In this paper, we present a comparison of a variety of approaches to estimating and postprocessing features. To further aid in discrimination of periodic signals from aperiodic signals, we add a differential energy term. We evaluate our approaches on the TUH EEG Corpus, which is the largest publicly available EEG corpus and an exceedingly challenging task due to the clinical nature of the data. We demonstrate that a variant of a standard filter bank-based approach, coupled with first and second derivatives, provides a substantial reduction in the overall error rate. The combination of differential energy and derivatives produces a 24% absolute reduction in the error rate and improves our ability to discriminate between signal events and background noise. This relatively simple approach proves to be comparable to other popular feature extraction approaches such as wavelets, but is much more computationally efficient.
TL;DR: This paper proposes the first use of the variable-Q transform (VQT) to generate the time–frequency representation for acoustic scene classification, and achieves a classification accuracy of 85.5%, which outperforms one of the top performing systems.
Abstract: In this paper, we present an approach for acoustic scene classification, which aggregates spectral and temporal features We do this by proposing the first use of the variable-Q transform (VQT) to generate the time–frequency representation for acoustic scene classification The VQT provides finer control over the resolution compared to the constant-Q transform (CQT) or short time fourier transform and can be tuned to better capture acoustic scene information We then adopt a variant of the local binary pattern (LBP), the adjacent evaluation completed LBP (AECLBP), which is better suited to extracting features from acoustic time–frequency images Our results yield a 52% improvement on the DCASE 2016 dataset compared to the application of standard CQT with LBP Fusing our proposed AECLBP with HOG features, we achieve a classification accuracy of 855%, which outperforms one of the top performing systems