Short-time Fourier Analysis Research Articles

Analysis of precursor response characteristics and time-frequency vibration parameters of rock failure: a laboratory experimental method

ABSTRACT To explore the vibration characteristics of rock failure precursors, a vibration optical force combined monitoring method was adopted. In this study, quasi-sandstone and granite samples were selected for uniaxial loading experiments to monitor the surface crack propagation and vibration. The variations in acceleration, displacement, energy, and waveform characteristics of quasi-sandstone and granite were studied from the perspective of the time domain. Additionally, a warning parameter SKE (Skewness, Kurtosis and Energy) based on waveform characteristics and energy was proposed. From the perspective of time-frequency analysis, a warning parameter CFEFD (Center Frequency Energy and Frequency Difference) is proposed by comprehensively utilising short-time Fourier analysis and statistical methods. The prediction effectiveness of both parameters was evaluated. It was observed that the vibration acceleration, energy, and displacement of rocks can all reflect the propagation of rock cracks. Prior to reaching the peak stress, the vibration acceleration and energy of rocks undergo sudden changes. The development of rock fractures releases energy, leading to high-energy frequency diffusion. Before reaching the peak stress, the range and energy level of high-energy frequency diffusion show significant improvement. Finally, it was found that both the SKE and CFEFD warning indicators can provide a certain degree of warning effect.

Perception of power quality disturbances using Fourier, Short-Time Fourier, continuous and discrete wavelet transforms

Electric power utilities must ensure a consistent and undisturbed supply of power, with the voltage levels adhering to specified ranges. Any deviation from these supply specifications can lead to malfunctions in equipment. Monitoring the quality of supplied power is crucial to minimize the impact of fluctuations in voltage. Variations in voltage or current from their ideal values are referred to as "power quality (PQ) disturbances," highlighting the need for vigilant monitoring and management. Signal processing methods are widely used for power system applications which include understanding of voltage disturbance signals and used for retrieval of signal information from the signals Different signal processing methods are used for extracting information about a signal. The method of Fourier analysis involves application of Fourier transform giving frequency information. The method of Short-Time Fourier analysis involves application of Short-Time Fourier transform (STFT) giving time–frequency information. The method of continuous wavelet analysis involves application of Continuous Wavelet transform (CWT) giving signal information in terms of scale and time where frequency is inversely related to scale. The method of discrete wavelet analysis involves application of Discrete Wavelet transform (DWT) giving signal information in terms of approximations and details where approximations and details are low and high frequency representation of original signal. In this paper, an attempt is made to perceive power quality disturbances in MATLAB using Fourier, Short-Time Fourier, Continuous Wavelet and Discrete Wavelet Transforms. Proper understanding of the signals can be possible by transforming the signals into different domains. An emphasis on application of signal processing techniques can be laid for power quality studies. The paper compares the results of each transform using MATLAB-based visualizations. The discussion covers the advantages and disadvantages of each technique, providing valuable insights into the interpretation of power quality disturbances. As the paper delves into the complexities of each method, it takes the reader on a journey of signal processing complexities, culminating in a nuanced understanding of power quality disturbances and their representations across various domains. The outcomes of this research, elucidated through energy values, 3D plots, and comparative analyses, contribute to a comprehensive understanding of power quality disturbances. The findings not only traverse theoretical domains but also find practical utility in real-world scenarios.

Perceptual and cognitive infiltration of piano lessons for college students under the concept of digital health

Abstract In this paper, firstly, according to mental health education, the research on the perception penetration of the piano course is illustrated from the two directions of body perception and mind perception. Secondly, it researches the music feature extraction in the piano course, obtains the amplitude, wavelength and frequency of the music feature according to the piano string vibration equation, then applies the short-time Fourier analysis to identify the music feature and reconstructs the piano timbre by using the identification result, which provides technical support for the design and realization of the piano course teaching system. Then, the overall design direction of the piano course teaching system is determined according to the teaching needs of the piano course in colleges and universities, and then the core module of the piano course system is realized. At the same time, the teaching of piano courses is empirically analyzed through statistical analysis. The results show that there is a significant difference between piano course teaching and the degree of perception and cognition in piano course teaching (F=4.194, p<0.05), there is a significant difference between piano course teaching and the degree of cognition (F=4.243, p<0.05), and there is a significant difference between piano course teaching and interpersonal relationship (F=5.391, p<0.05). This study not only has a promoting effect on piano teaching in colleges and universities but also improves the guiding value of piano curriculum reform in colleges and universities.

A Novel Adaptive STFT-SFA Based Fault Detection Method for Nonstationary Processes

In the field of fault detection, the nonstationary characteristics caused by external disturbances of wind turbines and other reasons can mask the fault signals, while the inconsistent data distribution between training data and test data due to equipment loss and other reasons can lead to model mismatch problems, both of which can lead to the degradation of fault detection performance. In order to solve the above problems, a novel adaptive fault detection framework is proposed in this work. First, the stationary features of nonstationary variables are extracted based on short-time Fourier analysis, after which the features are combined with the stationary variables. Second, isolation-based anomaly detection using nearest-neighbor ensembles is introduced as monitoring metrics for designing the statistic for the slow feature analysis method. Then, the differences between online normal data and training data are calculated, the model update factor and update strategy are designed, and an adaptive fault detection framework based on short-time Fourier transform-slow feature analysis (STFT-SFA) is proposed. Finally, the effectiveness of the proposed fault detection framework is verified using the Tennessee-Eastman process and actual wind turbine data. The results show that the proposed STFT-SFA method has a 94.0% correct monitoring rate (CMR) for Tennessee Eastman process failures, and the proposed adaptive STFT-SFA method has a 95.6% CMR for wind turbine failures, which are better than other comparative fault detection methods.

Quad-copter noise measurements under realistic flight conditions

This work experimentally studies the noise features of flying drones under representative working conditions, including hover, spin, climb, vertical flight, and cruise. Being different from outdoor experiments where many uncertain factors, such as gust wind and ground reflections, could affect test results, measurements in this study were conducted in a large anechoic chamber. The noise was measured with a ground-based rectangular microphone array covering an area of 5.6 m×6.4 m and a 4.6 m linear array on the sidewall to account for emission directivity. A binocular vision tracking system was used to trace drones' motion in real-time. Results show that a flying drone's noise feature is sensitive to its flight altitude, relative distance from observers, and motion status. Interestingly, under hovering conditions, the noise directivity is similar to that of an isolated propeller, and a low-noise region is found underneath the fuselage. While for cruising flight, the region with higher noise levels is observed behind the drone due to the fuselage's pitch angle. More importantly, the synchronized systems for noise and position measurements allow finding of an acoustic response to any change in motion status across flight tests. A coherence between real-time changes in the noise spectrogram and variations of the propeller rotating speed is found, thanks to the short-time Fourier analysis of both noise and motion-tracking data. The present finding extends the authors' previous work, enabling a deeper understanding of general drone noise features under different operating conditions.

Enhanced Detection of Bubble Emissions Through the Intact Spine for Monitoring Ultrasound-Mediated Blood-Spinal Cord Barrier Opening.

We previously developed short burst, phase keying (SBPK) focused ultrasound (FUS) to mitigate standing waves in the human vertebral canal. Here, we show microbubble emissions from these pulses can be detected through the human vertebral arch and that these pulses are effective for blood-spinal cord barrier (BSCB) opening. At f0 = 514kHz, circulating microbubbles were sonicated through ex vivo human vertebrae (60kPa-1MPa) using a dual-aperture approach and SBPK exposures engineered to incorporate pulse inversion (PI). Signals from a 250kHz receiver were analyzed using PI, short-time Fourier analysis and the maximum projection over the pulse train. In rats (n = 14), SBPK FUS+microbubbles was applied to 3 locations/spinal cord at fixed pressures (∼0.20-0.47MPa). MRI and histology were used to assess opening and tissue damage. In human vertebrae between 0.2-0.4MPa, PI amplified the microbubble/baseline ratio at f0/2 and 2f0 by 202 ± 40% (132-291%). This was maximal at 0.4MPa, coinciding with the onset of broadband emissions. In vivo, opening was achieved at 40/42 locations, with mean MRI enhancement of 46 ± 32%(16%-178%). Using PI, f0/2 was detected at 14/40 opening locations. At the highest pressures (f0/2 present) histology showed widespread bleeding throughout the focal region. At the lowest pressures, opening was achieved without bleeding. This study confirmed that PI can increase sensitivity to transvertebral detection of microbubble signals. Preliminary in vivo investigations show that SBPK FUS can increase BSCB permeability without tissue damage. SBPK is a clinically relevant pulse scheme and, in combination with PI, provides a means of mediating and monitoring BSCB opening noninvasively.

시변 최적 유한 임펄스 응답 필터 기반 단시간 푸리에 계수 추정

In this paper, a new short-time Fourier analysis method for quasi-periodic signals is proposed based on a time-varying optimal finite impulse response (FIR) filter. A new state space model of quasi-periodic signals is proposed by defining the stochastic model for the increment of a Fourier coefficient (FC) in order to deal with the biased variants of FCs. Then, the time-varying optimal FIR filter is applied to the proposed model to estimate the FC and its increments. Since the proposed state-space model and time-varying optimal FIR filter can be reduced to the previous state-space model and time invariant FIR filter, the proposed method can provide a more general solution than the existing FIR filter-based short-time Fourier analysis method. Through a numerical example, it is shown that the proposed method has better estimation performance and tracking ability for estimating FCs and their increments compared with existing approaches.

Improved Receding Horizon Fourier Analysis for Quasi-periodic Signals

In this paper, an efficient short-time Fourier analysis method for the quasi-periodic signals is proposed via an optimal fixed-lag finite impulse response (FIR) smoother approach using a receding horizon scheme. In order to deal with time-varying Fourier coefficients (FCs) of quasi-periodic signals, a state space model including FCs as state variables is augmented with the variants of FCs. Through an optimal fixed-lag FIR smoother, FCs and their increments are estimated simultaneously and combined to produce final estimates. A lag size of the optimal fixed-lag FIR smoother is chosen to minimize the estimation error. Since the proposed estimation scheme carries out the correction process with the estimated variants of FCs, it is highly probable that the smaller estimation error is achieved compared with existing approaches not making use of such a process. It is shown through numerical simulation that the proposed scheme has better tracking ability for estimating time-varying FCs compared with existing ones.

A new wavelet family based on second-order LTI-systems

In this paper, a new family of wavelets derived from the underdamped response of second-order Linear-Time-Invariant (LTI) systems is introduced. The most important criteria for a function or signal to be a wavelet is the ability to recover the original signal back from its continuous wavelet transform. We show that it is possible to recover back the original signal once the Second-Order Underdamped LTI (SOULTI) wavelet is applied to decompose the signal. It is found that the SOULTI wavelet transform of a signal satisfies a linear differential equation called the reconstructing differential equation, which is closely related to the differential equation that produces the wavelet. Moreover, a time-frequency resolution is defined based on two different approaches. The new transform has useful properties; a direct relation between the scale and the frequency, unique transform formulas that can be easily obtained for most elementary signals such as unit step, sinusoids, polynomials, and decaying harmonic signals, and linear relations between the wavelet transform of signals and the wavelet transform of their derivatives and integrals. The results obtained are presented with analytical and numerical examples. Signals with constant harmonics and signals with time-varying frequencies are analyzed, and their evolutionary spectrum is obtained. Contour mapping of the transform in the time-scale and the time-frequency domains clearly detects the change of the frequency content of the analyzed signals with respect to time. The results are compared with other wavelets results and with the short-time fourier analysis spectrograms. At the end, we propose the method of reverse wavelet transform to mitigate the edge effect.

Gestures Recognition from Sound Waves

We propose a new method to recognize gestures from sound waves. The main contribution of this paper is to recognize gestures based on the analysis of short-time Fourier transforms (STFT) using the Doppler effect to sense gestures. To do this, we generate an inaudible tone, which gets frequency-shift

Phase distortion resulting in a just noticeable difference in the perceived quality of speech

Common speech enhancement methods based on the short-time Fourier analysis–modification–synthesis (AMS) framework, modify the magnitude spectrum while keeping the phase spectrum unchanged. This is justified by an assumption that the phase spectrum can be seen as unimportant to speech quality, and hence the noisy phase spectrum can be used as a reasonable estimate of the clean phase spectrum in signal reconstruction. In this work we show, by using an ideal magnitude estimator, that corruption in the phase spectrum can still affect the quality of the resulting speech in low SNR environments. Furthermore, we quantify the distortion in the phase spectrum which can be tolerated before it begins to affect speech quality. This is done through a series of experiments, using both subjective and objective tests, and statistical analysis to evaluate the results. The results show that the phase spectrum computed from noisy speech can be used as an estimate of the phase spectrum of the clean signal without noticeably affecting perceived speech quality, only if the segmental SNR of the noisy speech signal is greater than 7 dB.

Methods and algorithms for video-based multi-point frequency measuring and mapping

Object vibrations and movements can be detected through changes in its luminance. In this paper, we demonstrate that we can obtain the vibration frequency of all vibrating targets in the sequence simultaneously through the analysis of local neighborhoods. The study is completed with a short-time Fourier analysis so that changes in the movement frequencies are also accounted. We also show that this information can be displayed like a color frequency map that can be superimposed to the video sequence providing a whole description of the analyzed sequence. The method can be used to analyze complex structures since their different vibrating parts can be visualized at a glance. The main algorithms, methods and some sequences are freely downloadable so that new applications and procedures can be implemented by the scientific community.

IJIREEICE - Electrical, Electronics, Instrumentation and Control

The traditional infrastructure of power system industry is presently going through large amount of change that consensus over the improvement of power quality for fulfilling this requirement electronically automated devices having high sensitivity toward the disturbance in the power system signals are under the major consensus of research activities. The low power quality signal is responsible for malfunctioning of equipments that result in reduction of production and rise in the cost of production. In the modern age electronic convertors, facts and other thyristor controlled devices is combine with the new power transmission schemes. That produces harmonics current of high frequency order and brings large amount of current and voltage quality distortion. In this paper we are investigating and implementing the Prony harmonics analysis algorithm for estimation of harmonics in the voltage cycle available during saturable core transformer energizing the result are validate and compared with the short time Fourier analysis our proposed approach found to be more accurate and aliquant for non linear discrete data signals. Many of the loads installed in present-day power systems are harmonic current generators. Combined with the impedance of the electrical system, the loads also produce harmonic voltages. The nonlinear loads may be viewed as both harmonic current generators and harmonic voltage generators. Until 1970s, speed control of AC motors was primarily achieved using belts and pulleys (9). Now, adjustable speed drives (ASDs) perform speed control functions very efficiently. ASDs are generators of large harmonic currents. Fluorescent lights uses less electrical energy for the same light output as incandescent lighting but produce substantial harmonic currents in the process. Due to increase of personal computer use it has resulted in harmonic current in commercial buildings. Harmonic distortion is no longer a phenomenon confined to industrial equipment and processes, where the first power quality concerns developed. Uninterruptible power supplies (UPSs), personal computers (PCs), and electronic and entertaining devices proliferate nowadays in commercial and residential installations. These special kinds of loads represent formidable sources of harmonic currents and they increase with the expanding use of video recorders, digital clocks, and other sensitive electronic equipment.

Research on Piano Music Signal Recognition Based on Short-Time Fourier Analysis

This paper starts with the basic process of music recognition to complete the study on extraction and realization of seven musical characteristics of the music features characterization, at the same time, the paper in-depth studies the pitch value duration, tonality characteristic extraction unit. Fourier analysis method based on short-time uses the computer programming for audio signal automatic analysis and processing, implements the characteristics recognition of the piano music playing, Experimental data show that the average recognition rate of algorithm is above 95% with the strong recognition ability, which provides the core technology support for developing the evaluation system of piano performance.

Sound Analysis and Synthesis for Audio Simulation System of Flight Simulator

Through simulating the feelings of vision, hearing, motion and force during flight, the flight simulator can build a realistic flight environment on the ground. Therefore, Flight simulators have an important role in pilot training field. Audio simulation system is a key component of the flight simulator, it has direct impact on the realism and immersion of flight simulation. In this paper, we introduced the development procedure of audio simulation system shortly. Software implementation and its key development technology were mainly focused for expressing the necessity of extracting sound sources from original cockpit recordings. Based on the development method and practical recording conditions, we dicussed sound short-time Fourier analysis and synthesis technology, and sound linear prediction analysis and synthesis technology in detail. The objective of using these technologies was to extract sound sources from original recordings and this was also the preparation step of sound simulation.

Reassigned spectrum-based feature extraction for GMM-based automatic chord recognition

Most existing automatic chord recognition systems use a chromagram in front-end processing and some sort of classifier (e.g., hidden Markov model, Gaussian mixture model (GMM), support vector machine, or other template matching technique). The vast majority of front-end algorithms derive acoustic features based on a standard short-time Fourier analysis and on mapping energy from the power spectrum, or from a constant-Q spectrum, to chroma bins. However, the accuracy of the resulting spectral representation is a crucial issue. In fact, conventional methods based on short-time Fourier analysis involve an intrinsic trade-off between time resolution and frequency resolution. This work investigates an alternative feature set based on time-frequency reassignment, which was applied in the past to speech processing tasks such as formant extraction. As shown in the following experiments, the reassigned spectrum provides a very accurate front-end for the GMM-based chord recognition system here investigated.

EEGgui: a program used to detect electroencephalogram anomalies after traumatic brain injury

BackgroundIdentifying and quantifying pathological changes in brain electrical activity is important for investigations of brain injury and neurological disease. An example is the development of epilepsy, a secondary consequence of traumatic brain injury. While certain epileptiform events can be identified visually from electroencephalographic (EEG) or electrocorticographic (ECoG) records, quantification of these pathological events has proved to be more difficult. In this study we developed MATLAB-based software that would assist detection of pathological brain electrical activity following traumatic brain injury (TBI) and present our MATLAB code used for the analysis of the ECoG.MethodsSoftware was developed using MATLAB(™) and features of the open access EEGLAB. EEGgui is a graphical user interface in the MATLAB programming platform that allows scientists who are not proficient in computer programming to perform a number of elaborate analyses on ECoG signals. The different analyses include Power Spectral Density (PSD), Short Time Fourier analysis and Spectral Entropy (SE). ECoG records used for demonstration of this software were derived from rats that had undergone traumatic brain injury one year earlier.ResultsThe software provided in this report provides a graphical user interface for displaying ECoG activity and calculating normalized power density using fast fourier transform of the major brain wave frequencies (Delta, Theta, Alpha, Beta1, Beta2 and Gamma). The software further detects events in which power density for these frequency bands exceeds normal ECoG by more than 4 standard deviations. We found that epileptic events could be identified and distinguished from a variety of ECoG phenomena associated with normal changes in behavior. We further found that analysis of spectral entropy was less effective in distinguishing epileptic from normal changes in ECoG activity.ConclusionThe software presented here was a successful modification of EEGLAB in the Matlab environment that allows detection of epileptiform ECoG signals in animals after TBI. The code allows import of large EEG or ECoG data records as standard text files and uses fast fourier transform as a basis for detection of abnormal events. The software can also be used to monitor injury-induced changes in spectral entropy if required. We hope that the software will be useful for other investigators in the field of traumatic brain injury and will stimulate future advances of quantitative analysis of brain electrical activity after neurological injury or disease.

Time-frequency analysis of wood anomalies in acoustics

When performing a Fourier transform on the reflection from a periodically corrugated surface for a normally incident beam the reflection spectrum is obtained. Steep dips have been observed from such reflection spectrum and are commonly called Wood anomaly. Because the frequencies at which Wood anomalies appears coincide with the frequencies where stimulation of Scholte-Stoneley waves is expected, Wood anomalies are traditionally explained as being caused by Scholte-Stoneley waves and as drain of energy. In this work, time-frequency domain analysis is made on the reflection from corrugated surfaces. The analysis suggests that Wood anomalies only occur when the entire reflection is processed by a Fourier transform, whereas for short time Fourier analysis this phenomenon does not seem to occur. The analysis also shows that energy is delayed in the time domain at the generation frequency of Scholte-Stoneley waves, and is therefore not necessarily drained. From the time-frequency point of view the source of Wood anomalies is re-examined and the actual relationship between the surface wave generation and Wood anomalies is also investigated.

An Efficient Speaker Recognition System for Separating the Single Channel Speech Using Frequency Modulation

Separating multiple speech signals from single channel sound signal made a huge challenge between researchers. In our paper, we intended a Modulation Frequency technique for Single channel Speech separation. Speech is perceived as having a pitch at the fundamental frequency of vibration of the vocal chords. The combined speech is first decomposed into a two-dimensional time-frequency representation using Short-time Fourier analysis (STFT) with frequency bins. It will pass through a narrow band pass Filter. A cross channel correlation did not use its fundamental frequency directly and while it uses tools to estimate pitch. A frequency bins contain fundamental frequency will correlate with the frequency bins with harmonics and the preset value to be initiated. A standard speaker identification system based on Gaussian Mixture models is of frame basis to identify the interfering speaker. A two parallel signal separation, one based on correlations of modulation frequency and the other based on fundamental frequency alone approaches is obtained. Our proposed method is implemented in MATLAB and verified using various speech signals. The experimental results show that the proposed technique is more efficient to separate the multiple voices from Single Channel Speech Signal.

The application of signal processing techniques for real-time monitoring of the dynamic stability of a ship via its motion responses

Significant changes of stability at sea can lead to dangerous situations and eventually stability failure. Despite its importance, the current intact stability criteria do not evaluate the motion responses of a vessel. More recently, the International Maritime Organization has identified phenomena in seaways responsible for stability failures. These phenomena can cause large roll angles and/or accelerations that can endanger ships due to critical stability situations in waves. The measurement of waves while a ship is underway is a major challenge, but ship motion is a good reflection of the wave characteristics and can be captured. Signal processing techniques are used in the detection and estimation of the influential parameters of a wave through the analysis of motion responses. Some variables of the system can be detected by spectral analysis of heave and pitch responses. These variables are the peak wave frequencies and associated magnitudes which can cause a high roll motion when similar to the roll natural frequency. The instantaneous frequency present in the signal is revealed through spectral analysis of short-time Fourier transforms in less than a minute. The instantaneous frequency is a parameter of practical importance which can be used in decision-making processes to avoid high roll motions.