Time-frequency Variation Research Articles

A study on the time–frequency variation law of electromagnetic radiation signals during concrete cracking

A study on the time–frequency variation law of electromagnetic radiation signals during concrete cracking

Hybrid CNN-BiLSTM architecture with multiple attention mechanisms to enhance speech emotion recognition

During recent years, the concept of attention in deep learning has been increasingly used to boost the performance of Speech Emotion Recognition (SER) models. However, these models for SER exhibit shortcomings in jointly emphasizing the time-frequency and dynamic sequential variations, often under-utilizing the rich contextual emotion-related information. We propose a hybrid deep learning model for SER using Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory Networks (BiLSTM) with multiple attention mechanisms. Our model utilizes features from the speech waveform viz. Mel spectrograms and Mel Frequency Cepstral Coefficients (MFCC), along with their time derivatives as input to the CNN and BiLSTM modules, respectively. A Time–Frequency Attention (TFA) mechanism, optimally incorporated into CNN, helps to selectively focus on emotion-related energy–time–frequency variations in Mel spectrograms. Attention-based BiLSTM uses MFCC and its time derivatives to identify the positional information of emotion for addressing the dynamic sequential variations. Finally, we fuse the attention-learned features from the CNN and BiLSTM modules and feed them to a Deep Neural Network (DNN) for SER. The experiments were carried out using three different datasets: Emo-DB and IEMOCAP, which are public datasets, and Amritaemo_Arabic; a private dataset. The hybrid model exhibited superior performance on both the public and private datasets, generating an average SER accuracy of 94.62%, 67.85%, and 95.80% with Emo-DB, IEMOCAP, and Amritaemo_Arabic datasets, respectively, effectively outperforming several state-of-the-art models.

High-Efficiency Positioning of Vibration Intrusions for Long-Distance Perimeter Security Monitoring Based on Time-Frequency Variation Envelopes

High-Efficiency Positioning of Vibration Intrusions for Long-Distance Perimeter Security Monitoring Based on Time-Frequency Variation Envelopes

Determination of duration, threshold and spatiotemporal distribution of extreme continuous precipitation in nine major river basins in China

China is one of the countries most severely affected by extreme precipitation, and it is urgent to carry out relevant research on the spatiotemporal distribution of extreme precipitation in China. Compared with single-day precipitation events, heavy precipitation events often last for multiple days and have greater potential disaster impacts, such as floods and mudslides. Therefore, in this study, we focus on continuous precipitation (CP) events for analysis. In order to determine the most probable duration of continuous heavy precipitation events, we innovatively adopted two time-frequency variation methods, namely Fast Fourier Transform (FFT) and Continuous Wavelet Transform (CWT). Then through Detrended Fluctuation Analysis (DFA) and multifractal DFA (MF-DFA) methods, extreme CP thresholds are extracted according to the fluctuation characteristics of the CP series. The results show that the average duration of heavy precipitation in the Pearl River Basin (PRB) area was the longest (up to 7.7 days), followed by the Continental Basin (CB) area (7.6 days). The Haihe River Basin (HRB) area had the shortest duration of heavy precipitation (5.0 days), followed by the Huaihe River Basin (HURB) area (5.9 days). The extreme CP threshold in the Southeast Basin (SEB) area was the largest (112.17 mm), followed by the PRB area (108.22 mm). The extreme CP threshold in the CB area was the smallest (20.17 mm), followed by the Yellow River Basin (YRB) area (44.31 mm). Through mutation and trend testing to evaluate the changing state of the extreme CP time series, it was found that, with one exception (HRB), the average extreme CP after the mutation in the watershed areas was larger than that before the mutation, and eight watersheds showed an upward trend in extreme CP. This suggests that most watershed areas in China are at risk of extreme CP increases. This study can provide an important reference for the analysis of extreme CP in China.

Precise disturbance localization of long distance fiber interferometer vibration senor based on an improved time–frequency variation feature extraction scheme

Locating intrusions with high accuracy is crucial for prompting long distance distributed optical fiber vibration sensing systems to practical applications in engineering filed. To achieve the high measurement accuracy and real-time performance simultaneously, in this paper, an intensity-modulated ultra-long distance distributed optical fiber vibration sensing system using an asymmetric dual Mach-Zehnder interferometer is experimentally established. More importantly, a positioning approach based on an ameliorated time–frequency feature extraction scheme is proposed with both theoretically analysis and experimental demonstration. In particular, an endpoint detection algorithm based on differential operation is firstly utilized to rapidly and accurately locate the endpoints of the received vibration signals. Thus the large-band width signals can be successfully acquired by setting an approximate window. Then, the ameliorated time–frequency feature extraction scheme based on a maximal overlap discrete wavelet packet transform is applied to eliminate the asymmetry of the large-band width signals, as it could lead to an erroneous positioning result when directly using a cross-correlation time delay estimation algorithm. Finally, the vibration positions along the sensing fiber link of the established sensing system can be successfully demodulated with the help of the extracted time–frequency features. To validate the effectiveness of the proposed scheme, vibration positioning tests were conducted in a real perimeter security scenario. And the results show that a mean positioning error of 11.12 m and a mean processing time of 0.276 s have been realized in a sensing length of 101 km. Thus, it is believed that through continuous technological advancements and refine algorithm optimization, the long distance distributed optical fiber vibration sensor founded on the proposed positioning strategy is poised to play an important role in the practical engineering applications.

Wide-frequency-range vibration positioning based on adaptive TQWT for long-distance asymmetric interferometer sensor

The asymmetric dual Mach–Zehnder interferometer (ADMZI) based vibration sensor effectively extends the sensing distance, however, the asymmetry seriously deteriorates the positioning accuracy. In this paper, an adaptive tunable Q-factor wavelet transform (TQWT) based long-distance asymmetric sensing system is proposed and experimentally demonstrated for the positioning of wide-frequency-range knocking vibration signals. The TQWT can extract the time-frequency features of the non-stationary signals by performing multi-scale decomposition. Firstly, the positioning method adaptively determines the decomposition levels by analyzing the power spectrum of the vibration signals, which not only effectively suppresses the effect of low-frequency noise, but also accurately extracts the main time-frequency variation characteristics of the vibration signals with various bandwidths. Then, the time delay between the time-frequency characteristics is obtained using a cross-correlation algorithm, and the vibration position is demodulated. Experimental results show that the method can accurately locate vibration signals with bandwidths of 10–80 kHz at a sensing length of 125 km. For high-frequency, strong vibration signals, the standard deviation of the positioning is around 35.1 m. For low-frequency, weak signals, the proposed method can still achieve effective positioning compared with the traditional approaches with a standard deviation of approximately 238.1 m. The proposed scheme can significantly improve the applicability of the asymmetric interferometer based vibration sensing system.

Numerical analysis of the internal flow and pressure pulsation characteristics of a submersible tubular electric pump device

In order to clarify the non-constant flow characteristics of the impeller and bulb body of the submersible tubular electric pump device, the entire flow rate conduit of the pump device is numerically calculated using the numerical simulation method, focusing on the analysis of the non-constant flow field characteristics of the guide vane body and bulb body and the time–frequency variation law of the pressure pulsation, and the results of the physical model testing confirm the validity of the numerical simulation. The findings demonstrate that the impeller of a submersible tubular electric pump is mostly responsible for the impeller’s inlet pressure pulsation, and the number of impeller blades to the number of peaks and valleys is consistent. Under the high flow rate condition of 1.2 Qd, the pressure fluctuation in the impeller inlet, between the impeller and the guide vane is small, and the main frequency is located at three times the rotational frequency, and the pressure pulsation at the outlet of the guide vane body has no obvious pattern and small amplitude. As the flow rate increases, the peak value of pressure pulsation at each monitoring point in the characteristic section of the pump device gradually decreases. The pressure pulsation peak value varies widely, ranging from 0.058 to 0.15, at each monitoring location of the impeller inlet. The peak value of pressure pulsation at each monitoring point of the impeller outlet fluctuates less due to the change of flow rate. The size and scale of the omega vortex structure in the guide vane body at different moments of the same cycle is small, and the number of vortex structures from the guide vane body inlet to the outlet direction shows a gradual increase in the trend; with a rise in flow rate, there is a tendency for the velocity and deflection angle of the guide vane body outlet and bulb body outlet surface to decrease.

Breast cancer detection and diagnosis using hybrid deep learning architecture

The detection of cancer regions in the breast is important to save the women patient’s life. In this paper, mammogram image is classified into normal, benign, and malignant using the proposed hybrid Convolutional Neural Networks (CNN) architecture. The proposed system consists of a radon transform, data augmentation module, and hybrid CNN architecture. The radon transform transforms each spatial pixel in the source mammogram image into a time–frequency variation image. This image is data augmented to construct a new dataset from the existing dataset to improve the breast cancer detection rate. The data augmented images are classified into three different cases using the proposed hybrid CNN architecture. Further, a mathematical morphological-based segmentation algorithm is used to segment the cancer pixels. In this article, Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) datasets for mammogram image analysis are used to estimate the performance efficiency of the developed deep learning architecture. The developed CNN architecture provides 97.91% Se, 97.83% Sp, 98.44% Acc, and 98.57% JI on the mammogram images available in the DDSM dataset. The developed CNN architecture provides 98% Se, 98.66% Sp, 99.17% Acc, and 98.07% JI on the mammogram images available in the MIAS dataset. For both open access datasets, the experimental results are compared to recent similar works. From the extensive analysis of experimental results of the proposed method, the methodologies presented in this article clearly segment the boundary of the cancer region in an abnormal mammogram image.

Loss Tangent Estimation From GPR Data in Complex Medium Scenarios Using Amplitude and Power Spectrum Centroid Frequency Shift Methods

Electromagnetic losses estimation in complex scenarios lacking subsurface continuous reflectors is challenging. We present here a robust approach based on the centroid frequency shift method and Ricker wavelet as the input signal, to compute intrinsic (absorption) losses. The centroid-frequency shift is calculated using the amplitude and power spectrum of records, leading to equations that relate the centroid frequency variation in time to the loss tangent (intrinsic losses). First, the approaches are evaluated and compared by implementation to synthetic Ground Penetrating Radar (GPR) data generated with the gprMax simulator, starting from an inhomogeneous material with spatially smooth dielectric variations. Then, they are applied to real GPR data collected on a fractured basaltic lava flow. The retrieved loss tangent values are compared to the total attenuation estimated from the signal amplitude decay, to evaluate the contribution due to scattering phenomena. The proposed analysis is particularly suitable for GPR data collected in planetary environments, since dielectric losses could be used to constrain the oxide composition of the material as well as scattering losses to determine the amount and distribution of subsurface inhomogeneities.

Responses of GNSS ZTD Variations to ENSO Events and Prediction Model Based on FFT-LSTME

The El Niño-Southern Oscillation (ENSO) event often causes natural disasters in mainland China. Existing quantitative analysis of ENSO events effects on climate change in mainland China is insufficient. The monthly scale prediction effectiveness of ENSO events is still low. Global Navigation Satellite System (GNSS) can estimate zenith tropospheric delay (ZTD) with high accuracy, which can study ZTD responses to ENSO and improve the prediction accuracy of ENSO events. This study quantitatively analyzed the response patterns of GNSS ZTD time-frequency variation to ENSO events in mainland China. The monthly multivariate ENSO index (MEI) thresholds for GNSS ZTD anomaly response to ENSO events are (-1.12,1.92) for the tropical monsoon zone, (-1.12,1.61) for the subtropical monsoon zone, (-1.19,1.62) for the temperate monsoon zone, (-1.26,1.64) for the temperate continental zone, and (-1.22,1.72) for the mountain plateau zone. The ENSO event causes the amplitude of the 9-month variation period to decrease and the amplitude of the 0.8–3-month period to increase for the GNSS ZTD in mainland China. Furthermore, a forecasting model is proposed with integrating fast Fourier transform and long short-term memory extended (FFT-LSTME). The model uses monthly MEI as the primary input and the GNSS ZTD reconstruction sequence that responds to ENSO as the auxiliary input. It can predict ENSO events in the next 24 months with an index of agreement (IA) of 91.56% and a root mean square error (RMSE) of 0.25. The RMSE is optimized by 70.48%, 43.95%, and 11.6% when compared with radial basis function (RBF), LSTM, and FFT-LSTM.

Polyphonic Sound Event Detection Using Temporal-Frequency Attention and Feature Space Attention.

The complexity of polyphonic sounds imposes numerous challenges on their classification. Especially in real life, polyphonic sound events have discontinuity and unstable time-frequency variations. Traditional single acoustic features cannot characterize the key feature information of the polyphonic sound event, and this deficiency results in poor model classification performance. In this paper, we propose a convolutional recurrent neural network model based on the temporal-frequency (TF) attention mechanism and feature space (FS) attention mechanism (TFFS-CRNN). The TFFS-CRNN model aggregates Log-Mel spectrograms and MFCCs feature as inputs, which contains the TF-attention module, the convolutional recurrent neural network (CRNN) module, the FS-attention module and the bidirectional gated recurrent unit (BGRU) module. In polyphonic sound events detection (SED), the TF-attention module can capture the critical temporal–frequency features more capably. The FS-attention module assigns different dynamically learnable weights to different dimensions of features. The TFFS-CRNN model improves the characterization of features for key feature information in polyphonic SED. By using two attention modules, the model can focus on semantically relevant time frames, key frequency bands, and important feature spaces. Finally, the BGRU module learns contextual information. The experiments were conducted on the DCASE 2016 Task3 dataset and the DCASE 2017 Task3 dataset. Experimental results show that the F1-score of the TFFS-CRNN model improved 12.4% and 25.2% compared with winning system models in DCASE challenge; the ER is reduced by 0.41 and 0.37 as well. The proposed TFFS-CRNN model algorithm has better classification performance and lower ER in polyphonic SED.

Frequency spectrum and energy refinement characteristics of blasting vibration signals in raw water pipeline tunnel excavation

The analysis of time-frequency variation and energy refinement characteristics of blasting vibration signals has contributed to understanding the propagation law of blasting vibration wave and reducing the possible losses. Combined with the measured data of tunnel blasting excavation and based on the newly constructed wavelet function, the spectrum distribution and energy refinement characteristics of tunnel blasting vibration signals are deeply explored and studied. The results demonstrated that compared to the Fourier spectrum, the innovative method of scale energy spectrum can not only acquire the dominant frequency of the blasting vibration signals, but also the obtained spectrum curve is smoother and can clearly reflect the change trend of the signal spectrum. The newly constructed biorthogonal wavelet has the characteristics of high vanishing moment, high regularity and matching with the waveform variation of the measured blasting vibration signals, and can describe the subtle variation characteristics of blasting vibration signal frequency. The continuous wavelet transform energy spectrum can reflect the three-dimensional energy distribution of blasting vibration signal in the time-scale domain, and the occurrence time of frequency, the frequency duration interval and time range of blasting vibration signal can also be acquired. Wavelet packet algorithm can precisely calculate the energy distribution of each frequency component in the signal, the tunnel blasting vibration signals (YBJ1, YBJ2) generated near the power tower presents low frequency, while the signals (YBF3,YBF4) far away from the power tower presents relatively high frequency. Measures should be taken to control the vibration and resonance of power tower caused by tunnel blasting. This research is of great significance for recognizing the propagation law of vibration waves, reducing the impact of blasting on surrounding buildings, and ensuring the safety of tunnel construction and surrounding buildings.

Power-Independent Microwave Instantaneous Frequency Measurement Based on Combination of Brillouin Gain and Loss Spectra

Photonics-assisted microwave instantaneous frequency measurement (IFM) is considered as a promising technique for detecting an unknown radio-frequency (RF) signal, which has advantages of broad frequency measurement range and fast processing speed. Up to date, there is no photonics-assisted IFM system that can essentially measure an unknown RF signal time-frequency information with power fluctuating seriously. Moreover, the measurement accuracy and frequency modulation recognition are not as good as those of traditional electronic methods. Here, we propose an IFM scheme based on the combination of Brillouin gain spectrum (BGS) and Brillouin loss spectrum (BLS), which is RF-power-independent with high measurement efficiency. The combination of BGS and BLS to construct the amplitude comparison function (ACF) can reduce the influence of RF signal power fluctuation within 21.27 dB by single-shot measuring. When an appropriate frequency interval with frequency domain monotonous power of microwave comb signal is modulated on the pump lightwave, the IFM accuracy of the unknown signal reaches about 1 MHz. After the initial calibration of the system, it can recognize the time-frequency variation of single tone signal, linear frequency modulated signal (LFM), nonlinear frequency modulated signal (NLFM), and Costas signal. This scheme will help to promote the application of the IFM in the field of spectrum reconnaissance and reception.

Multi-attribute seismic data spectrum analysis of tunnel orthogonal underpass landslide shaking table test

To explore the spatial deformation characteristics and dynamic response law of the tunnel's orthogonal underpass landslide under potential seismic action, we carried out the shaking table test for the first time. We measured and obtained the seismic data with various attributes, such as the characteristic image of different probability horizontal seismic action, acceleration, dynamic soil pressure and dynamic strain. Based on the time-domain analysis of multi-attribute data such as deformation characteristics and acceleration, we revealed the spatial dynamic response characteristics of the lining structure at different positions in the annular particle. By Wavelet Transform, we obtained the time-frequency variation relationship between the multi-attribute data. In addition, we discussed in detail the relationship between the frequency and the specific period of acceleration, dynamic soil pressure and dynamic strain at different probabilities of horizontal vibration stages. We then proposed the damage level correlation quantitatively characterized by the spectrum characteristics of multi-attribute data through Fast Fourier Transform and statistical probability scatter-matrix operations. The sensitivity difference of multi-attribute seismic data was significant. The time-history effect of dynamic soil pressure was the most sensitive and the time-history effect of dynamic strain had obvious hysteresis compared with acceleration. In the frequency domain of multi-attribute seismic data signals, the predominant frequencies were mainly concentrated in 1–10 Hz, and their dominant frequencies were significantly different. The singular boundary between low-frequency and high-frequency was 10 Hz, and the structures with frequencies between 15 and 20 Hz were relatively safe. Multi-attribute data response variables were positively correlated, acceleration was highly correlated with dynamic soil pressure response, while acceleration was weakly correlated with dynamic strain response, showing a significant correlation. Due to the spatial location of the tunnel's orthogonal underpass landslide, and the effect of seismic wave amplification and bias effect of mountain tunnel, the lining was damaged in a regional difference, and the inverted arch uplift cracked into a weak area. The research results can provide a theoretical reference for the failure model prediction and engineering safety evaluation of the tunnel's orthogonal underpass landslide in high intensity earthquake area.

Complex Principal Component Analysis of Antarctic Ice Sheet Mass Balance

Ice sheet changes of the Antarctic are the result of interactions among the ocean, atmosphere, and ice sheet. Studying the ice sheet mass variations helps us to understand the possible reasons for these changes. We used 164 months of Gravity Recovery and Climate Experiment (GRACE) satellite time-varying solutions to study the principal components (PCs) of the Antarctic ice sheet mass change and their time-frequency variation. This assessment was based on complex principal component analysis (CPCA) and the wavelet amplitude-period spectrum (WAPS) method to study the PCs and their time-frequency information. The CPCA results revealed the PCs that affect the ice sheet balance, and the wavelet analysis exposed the time-frequency variation of the quasi-periodic signal in each component. The results show that the first PC, which has a linear term and low-frequency signals with periods greater than five years, dominates the variation trend of ice sheet in the Antarctic. The ratio of its variance to the total variance shows that the first PC explains 83.73% of the mass change in the ice sheet. Similar low-frequency signals are also found in the meridional wind at 700 hPa in the South Pacific and the sea surface temperature anomaly (SSTA) in the equatorial Pacific, with the correlation between the low-frequency periodic signal of SSTA in the equatorial Pacific and the first PC of the ice sheet mass change in Antarctica found to be 0.73. The phase signals in the mass change of West Antarctica indicate the upstream propagation of mass loss information over time from the ocean–ice interface to the southward upslope, which mainly reflects ocean-driven factors such as enhanced ice–ocean interaction and the intrusion of warm saline water into the cavities under ice shelves associated with ice sheets which sit on retrograde slopes. Meanwhile, the phase signals in the mass change of East Antarctica indicate the downstream propagation of mass increase information from the South Pole toward Dronning Maud Land, which mainly reflects atmospheric factors such as precipitation accumulation.

Research on adaptive recommendation algorithm for big data mining based on Hadoop platform

Aiming at the problems of low data recall rate, poor data mining accuracy and poor redundant data interference ability of traditional data mining technology, an adaptive recommendation algorithm for big data mining based on Hadoop platform is proposed. In this algorithm, a distributed storage structure model of big data is constructed on Hadoop cloud platform; the statistical regression analysis method is adopted to construct a big data similarity mathematical model; the autocorrelation matching detection method is adopted to extract correlation features of big data and Backlund transform is adopted to decompose time-frequency variation characteristics of the correlation features of big data to design an adaptive recommendation model for big data mining based on Hadoop platform to optimise the performance of big data mining on Hadoop platform. The experimental results show that the accuracy of data mining increases with the number of iterations. Has a strong ability to redundant interference.

Time-Frequency Analysis of vertical and horizontal electric field changes of lightning negative return strokes observed in Sri Lanka

Simultaneously captured vertical and horizontal (total) electric field signatures of lightning negative Return Strokes (RS) were analyzed to obtain Time-Frequency (TF) variation using Stockwell Transformation (ST). In the study, ST was utilized since it is known to possess comparatively better time resolution at high frequency regions compared to other available TF methods. The data were obtained during the monsoon season of April–May 2014. The vertical and horizontal component of fifty negative RSs was utilized in the study. The resultant ST spectrum was analyzed and the regions of interest were demarcated based on the color which represented their relative power output intensities for different frequency components of the signal. The spread area was identified as the region of frequencies which consisted of power intensity equal or above 90th percentile when compared to the maximum possible value. The spectral area was identified as the area of frequencies in the borderline to the natural background noise. The spread region for the vertical E field had a range between 10 kHz and 650 kHz. Its average values were in between 126 kHz and 331 kHz. The spectral region of the vertical electric field change spanned from 1 kHz to 1020 kHz. Its average distribution was 44 kHz–660 kHz. Horizontal electric fields had a range of 20 kHz–1940 kHz in the spectral region. The same for the spread region was 80 kHz–910 kHz. The averages of the horizontal E field's spectral region were 46–1112 kHz and its spread region varied between 227 and 599 kHz. The results display a higher frequency range for all aspects of the horizontal E field changes which implies that its influence on the high frequency radiation is much higher than its vertical counterpart.

Localization measures in the time-scale setting

Wavelet (or continuous wavelet) transform is superior to the Fourier transform and the windowed (or short-time Fourier) transform because of its ability to measure the time–frequency variations in a signal at different time–frequency resolutions. However, the uncertainty principles in Fourier analysis set a limit to the maximal time–frequency resolution. We present some forms of uncertainty principles for functions that are \(\varepsilon \)-concentrated in a given region within the time–frequency plane involving particularly localization operators. Moreover we show how the eigenfunctions of such localization operators are maximally time–frequency-concentrated in the region of interest and we will use it to approximate such \(\varepsilon \)-concentrated functions.

Influence of winter North-Atlantic Oscillation on Climate-Related-Energy penetration in Europe

When considering 100% renewable scenarios, backup generation is needed for stabilizing the network when Climate Related Energy (CRE) such as wind, solar or run-of-the river hydropower are not sufficient for supplying the load. Several studies show that, over relatively short time period (less than 10 years), backup generation needs are reduced by dissipating power densities either in space through grids or time through storage. This study looks at the impact of low time frequency variations of CRE with a specific focus on the time variability induced by the North Atlantic Oscillation (NAO) teleconnection pattern during winter season. A set of eleven regions in Europe and Tunisia is used for highlighting space variability of the winter NAO’s impact. For each of these regions, we combine data from the Weather Research and Forecasting Model and the European Climate Assessment & Dataset for estimating solar-power, wind-power, run-of-the-river hydro-power and the energy load over the 1980–2012 time period. Results show that NAO’s impact on winter penetration rate depends on both the considered energy source and the location. They also highlight a non-linear relation between the NAO’s impact on CRE penetration rates and the level of equipment used for harvesting the CRE sources.

A multichannel time–frequency and multi-wavelet toolbox for uterine electromyography processing and visualisation

The uterine electromyogram, also called electrohysterogram (EHG), is an electrical signal generated by the uterine contractile activity. The EHG has been considered a promising biomarker for labour and preterm labour prediction, for which there is a demand for accurate estimation methods. Preterm labour is a significant public health concern and one of the major causes of neonatal mortality and morbidity [1]. Given the non-stationary properties of the EHG signal, time–frequency domain analysis can be used. For real life signals it is not generally possible to determine a priori the suitable quadratic time–frequency kernel or the appropriate wavelet family and relative parameters, regarding, for instance, the adequate detection of the signal frequency variation in time. There has been a lack of a comprehensive software tool for the selection of the appropriate time frequency representation of a multichannel EHG signal and extraction of relevant spectral and temporal information. The presented toolbox (Uterine Explorer) has been specifically designed for the EHG analysis and exploration in view of the characterisation of its components. The starting point is the multichannel scalogram or spectrogram representation from which frequency and time marginals, instantaneous frequency and bandwidth are obtained as EHG features. From this point the detected components undergo parametric and non-parametric spectral estimation and wavelet packet analysis. Intrauterine pressure estimation (IUP) is obtained using the Teager, RMS, wavelet marginal and Hilbert operators over the EHG. This toolbox has been tested to build up a dictionary of 288 EHG components [2], useful for research in preterm labour prediction.