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Journal articles on the topic 'Fourier spectrogram'

Author: Grafiati

Published: 19 February 2023

Last updated: 19 July 2025

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1

Pethiyagoda, Ravindra, Scott W. McCue, and Timothy J. Moroney. "Spectrograms of ship wakes: identifying linear and nonlinear wave signals." Journal of Fluid Mechanics 811 (December 6, 2016): 189–209. http://dx.doi.org/10.1017/jfm.2016.753.

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A spectrogram is a useful way of using short-time discrete Fourier transforms to visualise surface height measurements taken of ship wakes in real-world conditions. For a steadily moving ship that leaves behind small-amplitude waves, the spectrogram is known to have two clear linear components, a sliding-frequency mode caused by the divergent waves and a constant-frequency mode for the transverse waves. However, recent observations of high-speed ferry data have identified additional components of the spectrograms that are not yet explained. We use computer simulations of linear and nonlinear s

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Yu, Youxin, Wenbo Zhu, Xiaoli Ma, et al. "Recognition of Sheep Feeding Behavior in Sheepfolds Using Fusion Spectrogram Depth Features and Acoustic Features." Animals 14, no. 22 (2024): 3267. http://dx.doi.org/10.3390/ani14223267.

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In precision feeding, non-contact and pressure-free monitoring of sheep feeding behavior is crucial for health monitoring and optimizing production management. The experimental conditions and real-world environments differ when using acoustic sensors to identify sheep feeding behaviors, leading to discrepancies and consequently posing challenges for achieving high-accuracy classification in complex production environments. This study enhances the classification performance by integrating the deep spectrogram features and acoustic characteristics associated with feeding behavior. We conducted t

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Han, Ying, Qiao Wang, Jianping Huang, et al. "Frequency Extraction of Global Constant Frequency Electromagnetic Disturbances from Electric Field VLF Data on CSES." Remote Sensing 15, no. 8 (2023): 2057. http://dx.doi.org/10.3390/rs15082057.

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The electromagnetic data observed with the CSES (China Seismo-Electromagnetic Satellite, also known as Zhangheng-1 satellite) contain numerous spatial disturbances. These disturbances exhibit various shapes on the spectrogram, and constant frequency electromagnetic disturbances (CFEDs), such as artificially transmitted very-low-frequency (VLF) radio waves, power line harmonics, and interference from the satellite platform itself, appear as horizontal lines. To exploit this feature, we proposed an algorithm based on computer vision technology that automatically recognizes these lines on the spe

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Tucker, Jeff, Kathleen E. Wage, John R. Buck, and Lora J. Van Uffelen. "Performance weighted blended spectrogram." Journal of the Acoustical Society of America 157, no. 3 (2025): 2106–16. https://doi.org/10.1121/10.0036216.

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Spectrograms are used for time-frequency analysis and as preprocessing for signal classifiers and other algorithms. The conventional spectrogram is a tapered short-time Fourier transform, equivalent to a bank of bandpass filters. The taper defines filter-bank characteristics such as bandwidth and sidelobe levels. Although the conventional spectrogram uses minimal computational resources, its design requires a compromise between resolution and interference suppression. Adaptive spectrogram algorithms adjust the filter-bank based on incoming data, thereby allowing different bandwidth/sidelobe tr

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Wen, X., and M. Sandler. "Composite spectrogram using multiple Fourier transforms." IET Signal Processing 3, no. 1 (2009): 51. http://dx.doi.org/10.1049/iet-spr:20070015.

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Wen-kai Lu and Qiang Zhang. "Deconvolutive Short-Time Fourier Transform Spectrogram." IEEE Signal Processing Letters 16, no. 7 (2009): 576–79. http://dx.doi.org/10.1109/lsp.2009.2020887.

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Trufanov, N. N., D. V. Churikov, and O. V. Kravchenko. "Selection of window functions for predicting the frequency pattern of vibrations of the technological process using an artificial neural network." Journal of Physics: Conference Series 2091, no. 1 (2021): 012074. http://dx.doi.org/10.1088/1742-6596/2091/1/012074.

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Abstract The frequency pattern of the process is investigated by analyzing spectrograms constructed using the window Fourier transform. A set of window functions consists of a rectangular, membership, and windows based on atomic functions. The fulfillment of the condition for improving the time localization and energy concentration in the central part of the window allows one to select a window function. The resulting spectrograms are fed to the input of an artificial neural network to obtain a forecast. Varying the shape of the window functions allows us to analyze the proposed spectrogram pr

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Dusek, Daniel. "Decomposition of Non-Stationary Signals Based on the Cochlea Function Principle." Solid State Phenomena 147-149 (January 2009): 594–99. http://dx.doi.org/10.4028/www.scientific.net/ssp.147-149.594.

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This paper deal with possibility of cochlea function principle utilization for decomposition any non-stationary signals. The mathematical model based on array of resonators is described in this paper. This array of resonators is actuated by non-stationary signal, which is compound from different frequency components. Spectrograms calculated for different values of resonators viscous damping are results of this work and this results are also compared with spectrogram obtained from Short Time Fourier Transformation (STFT).

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China Venkateswarlu, Guide: Dr S. "Speech Enhancement Using Spectrogram Denoising with Deep U-Net Architectures." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48929.

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Abstract -- Acoustic noise significantly degrades speech quality and intelligibility in almost all applications, ranging from telecommunications to voice assistants. In this paper, we address this problem by designing an efficient speech enhancement system based on deep learning. Our approach relies on spectrogram denoising, wherein audio signals are represented as 2D magnitude spectrograms that well maintain signal structure and enable direct application of Convolutional Neural Networks (CNNs). The backbone of our system is a U-Net model, which is a strong deep convolutional autoencoder capab

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Lyon, Douglas. "The Discrete Fourier Transform, Part 5: Spectrogram." Journal of Object Technology 9, no. 1 (2010): 15. http://dx.doi.org/10.5381/jot.2010.9.1.c2.

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Lu, Wenkai, and Fangyu Li. "Seismic spectral decomposition using deconvolutive short-time Fourier transform spectrogram." GEOPHYSICS 78, no. 2 (2013): V43—V51. http://dx.doi.org/10.1190/geo2012-0125.1.

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The spectral decomposition technique plays an important role in reservoir characterization, for which the time-frequency distribution method is essential. The deconvolutive short-time Fourier transform (DSTFT) method achieves a superior time-frequency resolution by applying a 2D deconvolution operation on the short-time Fourier transform (STFT) spectrogram. For seismic spectral decomposition, to reduce the computation burden caused by the 2D deconvolution operation in the DSTFT, the 2D STFT spectrogram is cropped into a smaller area, which includes the positive frequencies fallen in the seismi

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Kwon, Daehyun, Hanbit Kang, Dongwoo Lee, and Yoon-Chul Kim. "Deep learning-based prediction of atrial fibrillation from polar transformed time-frequency electrocardiogram." PLOS ONE 20, no. 3 (2025): e0317630. https://doi.org/10.1371/journal.pone.0317630.

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Portable and wearable electrocardiogram (ECG) devices are increasingly utilized in healthcare for monitoring heart rhythms and detecting cardiac arrhythmias or other heart conditions. The integration of ECG signal visualization with AI-based abnormality detection empowers users to independently and confidently assess their physiological signals. In this study, we investigated a novel method for visualizing ECG signals using polar transformations of short-time Fourier transform (STFT) spectrograms and evaluated the performance of deep convolutional neural networks (CNNs) in predicting atrial fi

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Palupi, Indiati Retno, and Wiji Raharjo. "The Utilization of Signal Analysis by Using Short Time Fourier Transform." RSF Conference Series: Engineering and Technology 1, no. 1 (2021): 30–36. http://dx.doi.org/10.31098/cset.v1i1.445.

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Signal Analysis is a part of geophysics work. It is important in analyse the character of signal or waveform in geophysics. In this paper the earthquake waveform is used as the example. One method to do this is used Short Time Fourier Transform. It adopts the basic concept of Fast Fourier Transform in the short period of time in waveform and at the same moment there is a convolutional process between the waveform and the mother wavelet and then resulting the spectrogram. Finally, the spectrogram will show the power spectrum or the magnitude of the amplitude in each time in the waveform. It rel

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Liu, Haohe, Xubo Liu, Qiuqiang Kong, Wenwu Wang, and Mark D. Plumbley. "Learning Temporal Resolution in Spectrogram for Audio Classification." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 12 (2024): 13873–81. http://dx.doi.org/10.1609/aaai.v38i12.29294.

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The audio spectrogram is a time-frequency representation that has been widely used for audio classification. One of the key attributes of the audio spectrogram is the temporal resolution, which depends on the hop size used in the Short-Time Fourier Transform (STFT). Previous works generally assume the hop size should be a constant value (e.g., 10 ms). However, a fixed temporal resolution is not always optimal for different types of sound. The temporal resolution affects not only classification accuracy but also computational cost. This paper proposes a novel method, DiffRes, that enables diffe

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Neralla, Manikanta. "Design and Performance Analysis of Short Time Fourier Transform Processor." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (2022): 3205–15. http://dx.doi.org/10.22214/ijraset.2022.41917.

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Abstract: Time-frequency domain characterization of signals have always been focused on variants of Short time Fourier transform (STFT). The selection of transform kernel plays an important role in preserving the signal support which provides a cross-term free time-frequency distribution. Time-Bandwidth product has been taken as a measure of signal support preservation criteria thereby developing an optimal kernel for STFT based on linear canonical decomposition. In the development of kernel , Fractional Fourier Transform (FrFT) is used which provides noise free frequency domain representation

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Safdar, Muhammad Farhan, Robert Marek Nowak, and Piotr Pałka. "A Denoising and Fourier Transformation-Based Spectrograms in ECG Classification Using Convolutional Neural Network." Sensors 22, no. 24 (2022): 9576. http://dx.doi.org/10.3390/s22249576.

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The non-invasive electrocardiogram (ECG) signals are useful in heart condition assessment and are found helpful in diagnosing cardiac diseases. However, traditional ways, i.e., a medical consultation required effort, knowledge, and time to interpret the ECG signals due to the large amount of data and complexity. Neural networks have been shown to be efficient recently in interpreting the biomedical signals including ECG and EEG. The novelty of the proposed work is using spectrograms instead of raw signals. Spectrograms could be easily reduced by eliminating frequencies with no ECG information.

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Feng, Sheng, Xiaoqiang Hua, and Xiaoqian Zhu. "Matrix Information Geometry for Spectral-Based SPD Matrix Signal Detection with Dimensionality Reduction." Entropy 22, no. 9 (2020): 914. http://dx.doi.org/10.3390/e22090914.

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In this paper, a novel signal detector based on matrix information geometric dimensionality reduction (DR) is proposed, which is inspired from spectrogram processing. By short time Fourier transform (STFT), the received data are represented as a 2-D high-precision spectrogram, from which we can well judge whether the signal exists. Previous similar studies extracted insufficient information from these spectrograms, resulting in unsatisfactory detection performance especially for complex signal detection task at low signal-noise-ratio (SNR). To this end, we use a global descriptor to extract ab

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Htee, Mu Wah, and Wut Yee Thinzar. "Frequency Analysis of Myanmar Bamboo Xylophone (Pattalar)." Bago University Research Journal Vol.10, No.1, no. 2020 (2020): 259–63. https://doi.org/10.5281/zenodo.3923422.

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Myanmar Bamboo Xylophone (Pattalar) is one of the famous Myanmar fixed-pitch musical instruments. In this paper, the frequency contents such as, pitch, harmonics and their relative amplitude of individual note of Pattalar are analyzed by Fourier analysis. The frequencies changing over time, in other words time-frequency representation of each tone, is visualized by spectrogram method based on short time Fourier transform (STFT).

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Albasu, Faisal, Mikhail Kulyabin, Aleksei Zhdanov, et al. "Electroretinogram Analysis Using a Short-Time Fourier Transform and Machine Learning Techniques." Bioengineering 11, no. 9 (2024): 866. http://dx.doi.org/10.3390/bioengineering11090866.

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Electroretinography (ERG) is a non-invasive method of assessing retinal function by recording the retina’s response to a brief flash of light. This study focused on optimizing the ERG waveform signal classification by utilizing Short-Time Fourier Transform (STFT) spectrogram preprocessing with a machine learning (ML) decision system. Several window functions of different sizes and window overlaps were compared to enhance feature extraction concerning specific ML algorithms. The obtained spectrograms were employed to train deep learning models alongside manual feature extraction for more classi

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Ding, Congzhang, Yong Jia, Guolong Cui, Chuan Chen, Xiaoling Zhong, and Yong Guo. "Continuous Human Activity Recognition through Parallelism LSTM with Multi-Frequency Spectrograms." Remote Sensing 13, no. 21 (2021): 4264. http://dx.doi.org/10.3390/rs13214264.

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According to the real-living environment, radar-based human activity recognition (HAR) is dedicated to recognizing and classifying a sequence of activities rather than individual activities, thereby drawing more attention in practical applications of security surveillance, health care and human–computer interactions. This paper proposes a parallelism long short-term memory (LSTM) framework with the input of multi-frequency spectrograms to implement continuous HAR. Specifically, frequency-division short-time Fourier transformation (STFT) is performed on the data stream of continuous activities

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Burriel-Valencia, Jordi, Ruben Puche-Panadero, Javier Martinez-Roman, Angel Sapena-Baño, Martin Riera-Guasp, and Manuel Pineda-Sánchez. "Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines." Energies 12, no. 17 (2019): 3361. http://dx.doi.org/10.3390/en12173361.

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Induction machines drive many industrial processes and their unexpected failure can cause heavy production losses. The analysis of the current spectrum can identify online the characteristic fault signatures at an early stage, avoiding unexpected breakdowns. Nevertheless, frequency domain analysis requires stable working conditions, which is not the case for wind generators, motors driving varying loads, and so forth. In these cases, an analysis in the time-frequency domain—such as a spectrogram—is required for detecting faults signatures. The spectrogram is built using the short time Fourier

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Merchan, Fernando, Ariel Guerra, Héctor Poveda, Héctor M. Guzmán, and Javier E. Sanchez-Galan. "Bioacoustic Classification of Antillean Manatee Vocalization Spectrograms Using Deep Convolutional Neural Networks." Applied Sciences 10, no. 9 (2020): 3286. http://dx.doi.org/10.3390/app10093286.

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We evaluated the potential of using convolutional neural networks in classifying spectrograms of Antillean manatee (Trichechus manatus manatus) vocalizations. Spectrograms using binary, linear and logarithmic amplitude formats were considered. Two deep convolutional neural networks (DCNN) architectures were tested: linear (fixed filter size) and pyramidal (incremental filter size). Six experiments were devised for testing the accuracy obtained for each spectrogram representation and architecture combination. Results show that binary spectrograms with both linear and pyramidal architectures wit

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Park, Dongsuk, Seungeui Lee, SeongUk Park, and Nojun Kwak. "Radar-Spectrogram-Based UAV Classification Using Convolutional Neural Networks." Sensors 21, no. 1 (2020): 210. http://dx.doi.org/10.3390/s21010210.

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With the upsurge in the use of Unmanned Aerial Vehicles (UAVs) in various fields, detecting and identifying them in real-time are becoming important topics. However, the identification of UAVs is difficult due to their characteristics such as low altitude, slow speed, and small radar cross-section (LSS). With the existing deterministic approach, the algorithm becomes complex and requires a large number of computations, making it unsuitable for real-time systems. Hence, effective alternatives enabling real-time identification of these new threats are needed. Deep learning-based classification m

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Kamiel, Berli Paripurna, and Muhammad Rizki Fadilah. "Application of Short Time Fourier Transform (STFT) For Diagnosing Rolling Bearing Faults." JMPM (Jurnal Material dan Proses Manufaktur) 7, no. 2 (2023): 118–27. http://dx.doi.org/10.18196/jmpm.v7i2.19813.

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A fan is crucial for maintaining airflow in industries. Bearings in fans prevent friction and must be robust to function effectively. Damage to the bearings can diminish machine performance. Predictive maintenance is essential for early detection of faults. One way to analyze bearing faults is by using the Short Time Fourier Transform (STFT), as it excels in analyzing non-stationary signals. Experiments were conducted under normal conditions and with inner race faults in bearings at a shaft speed of 1162.5 Hz. Vibration detection was done using an accelerometer sensor, and Matlab analysis was

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Gao, Zhi Bin. "Short Time Fourier Transform Analysis of Multi-Component Nonstationary Acoustic Signal." Advanced Materials Research 403-408 (November 2011): 3163–65. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3163.

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In order to extract major components of signal, multi-component nonstationary acoustic signal was analyzed with time-frequency analysis technique. By transforming multi-component nonstationary acoustic signal from time domain to time-frequency domain with short time Fourier transform, major components were determined according to spectrogram. Results show that major components and its time-frequency characteristic parameters can be extracted exactly. Short time Fourier transform is an effective method for extracting major components of nonstationary acoustic signal.

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Li, Pingzheng, Yanqun Wu, Wei Guo, et al. "Striation-Based Beamforming with Two-Dimensional Filtering for Suppressing Tonal Interference." Journal of Marine Science and Engineering 11, no. 11 (2023): 2117. http://dx.doi.org/10.3390/jmse11112117.

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Based on the interference spectrogram in the element–frequency domain using the data measured by the horizontal linear array, the source range can be estimated through the striation-based beamforming (SBF) method and its variants. Estimation of the striation slope is the basis for these ranging methods. But in practical scenarios, the tonal interferences and other noise make it difficult to estimate the slope. In this paper, we proposed a two-dimensional low-pass filtering method after the two-dimensional discrete Fourier transform (2D-DFT) of the element–frequency domain spectrogram. The sign

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Chi, Tai-Shih, and Chung-Chien Hsu. "Multiband analysis and synthesis of spectro-temporal modulations of Fourier spectrogram." Journal of the Acoustical Society of America 129, no. 5 (2011): EL190—EL196. http://dx.doi.org/10.1121/1.3565471.

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Han, Ying, Yalan Li, Jing Yuan, et al. "Automatic Recognition of Vertical-Line Pulse Train from China Seismo-Electromagnetic Satellite Based on Unsupervised Clustering." Atmosphere 14, no. 8 (2023): 1296. http://dx.doi.org/10.3390/atmos14081296.

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Pulse signals refer to electromagnetic waveforms with short duration and high peak energy in the time domain. Spatial electromagnetic pulse interference signals can be caused by various factors such as lightning, arc discharge, solar disturbances, and electromagnetic disturbances in space. Pulse disturbance signals appear as instantaneous, high-energy vertical-line pulse trains (VLPTs) on the spectrogram. This paper uses computer vision techniques and unsupervised clustering algorithms to process and analyze VLPT on very-low-frequency (VLF) waveform spectrograms collected by the China Seismo-E

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Yegnanarayana, B. "Group delay spectrogram of speech signals without phase wrapping." Journal of the Acoustical Society of America 151, no. 3 (2022): 2181–91. http://dx.doi.org/10.1121/10.0009922.

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This paper proposes a method for displaying the phase information in speech signals through group delay spectrogram, without the need for phase unwrapping. The method involves scaling down the phase values without affecting the shape of the phase or group delay function, thus preserving the information of the phase spectrum. This is accomplished using single-frequency filtering (SFF) of speech signals to obtain the instantaneous complex SFF spectrum. The SFF involves filtering a frequency-shifting signal using a resonator at half the sampling frequency. The SFF spectrum displays characteristic

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Debbal, S. M., and F. Bereksi-Reguig. "COMPLEMENTARY ANALYSIS TO HEART SOUNDS WHILE USING THE SHORT TIME FOURIER AND THE CONTINUOUS WAVELET TRANSFORMS." Biomedical Engineering: Applications, Basis and Communications 19, no. 05 (2007): 331–39. http://dx.doi.org/10.4015/s1016237207000434.

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This paper presents the analysis and comparisons of the short time Fourier transform (STFT) and the continuous wavelet transform techniques (CWT) to the four sounds analysis (S1, S2, S3 and S4). It is found that the spectrogram short-time Fourier transform (STFT), cannot perfectly detect the internals components of these sounds that the continuous wavelet transform. However, the short time Fourier transform can provide correctly the extent of time and frequency of these four sounds. Thus, the STFT and the CWT techniques provide more features and characteristics of the sounds that will hemp phy

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DEBBAL, S. M., and F. BEREKSI-REGUIG. "SECOND CARDIAC SOUND ANALYSIS TECHNIQUES AND PERFORMANCE COMPARISON." Journal of Mechanics in Medicine and Biology 05, no. 03 (2005): 429–42. http://dx.doi.org/10.1142/s021951940500162x.

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This paper presents the applications of the spectrogram, Wigner distribution and wavelet transform analysis methods to the second cardiac sound S2 of the phonocardiogram signal (PCG). A comparison between these methods has shown the resolution differences between them. It is found that the spectrogram Short-Time Fourier Transform (STFT) cannot detect the two internals components of the second sound S2 (A2 and P2, atrial and pulmonary components respectively). The Wigner Distribution (WD) can provide time-frequency characteristics of the sound S2, but with insufficient diagnostic information as

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Mandal, Sunandan, Kavita Thakur, Bikesh Kumar Singh, and Heera Ram. "Performance Evaluation of Spectrogram Based Epilepsy Detection Techniques Using Gray Scale Features." Journal of Ravishankar University (PART-B) 33, no. 1 (2020): 01–07. http://dx.doi.org/10.52228/jrub.2020-33-1-1.

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Electroencephalogram (EEG) is most common instrument for treatment and diagnosis of brain related diseases. Analysis of EEG signals for treatment of patient is time consuming and not easy task for neurologist. There is always a chance of human error. The purpose of this paper is to present an automatic detection model for epileptic seizure from EEG signals. To fulfill this objective, EEG signals are preprocessed and converted into spectrogram images using Short Time Fourier Transform (STFT). From this spectrogram images gray scale features are extracted. Support Vector Machine (SVM) with six d

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Yousufi, Musyyab, Robertas Damaševičius, and Rytis Maskeliūnas. "Multimodal Fusion of EEG and Audio Spectrogram for Major Depressive Disorder Recognition Using Modified DenseNet121." Brain Sciences 14, no. 10 (2024): 1018. http://dx.doi.org/10.3390/brainsci14101018.

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Background/Objectives: This study investigates the classification of Major Depressive Disorder (MDD) using electroencephalography (EEG) Short-Time Fourier-Transform (STFT) spectrograms and audio Mel-spectrogram data of 52 subjects. The objective is to develop a multimodal classification model that integrates audio and EEG data to accurately identify depressive tendencies. Methods: We utilized the Multimodal open dataset for Mental Disorder Analysis (MODMA) and trained a pre-trained Densenet121 model using transfer learning. Features from both the EEG and audio modalities were extracted and con

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Zhang, Feiyu, Luyang Zhang, Hongxiang Chen, and Jiangjian Xie. "Bird Species Identification Using Spectrogram Based on Multi-Channel Fusion of DCNNs." Entropy 23, no. 11 (2021): 1507. http://dx.doi.org/10.3390/e23111507.

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Deep convolutional neural networks (DCNNs) have achieved breakthrough performance on bird species identification using a spectrogram of bird vocalization. Aiming at the imbalance of the bird vocalization dataset, a single feature identification model (SFIM) with residual blocks and modified, weighted, cross-entropy function was proposed. To further improve the identification accuracy, two multi-channel fusion methods were built with three SFIMs. One of these fused the outputs of the feature extraction parts of three SFIMs (feature fusion mode), the other fused the outputs of the classifiers of

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Żakowski, Krzysztof, and Kazimierz Darowicki. "Detection of Stray Current Field Interference on Metal Constructions Using STFT." Key Engineering Materials 293-294 (September 2005): 785–0. http://dx.doi.org/10.4028/www.scientific.net/kem.293-294.785.

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A method of detection of stray currents using the Short Time Fourier Transformation (STFT) is presented. This particular kind of signal analysis makes the determination of changes of the spectral power density of a signal (e.g. structure to electrolyte potential) in function of time possible. The results of joint time-frequency analysis of the potential in the field of stray currents generated by tram-line are presented. The spectrogram is a composition of spectral lines of defined frequency distribution. A good correlation of localization of spectral lines corresponding to rail potential and

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Nisar, Shibli, Omar Usman Khan, and Muhammad Tariq. "An Efficient Adaptive Window Size Selection Method for Improving Spectrogram Visualization." Computational Intelligence and Neuroscience 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/6172453.

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Short Time Fourier Transform (STFT) is an important technique for the time-frequency analysis of a time varying signal. The basic approach behind it involves the application of a Fast Fourier Transform (FFT) to a signal multiplied with an appropriate window function with fixed resolution. The selection of an appropriate window size is difficult when no background information about the input signal is known. In this paper, a novel empirical model is proposed that adaptively adjusts the window size for a narrow band-signal using spectrum sensing technique. For wide-band signals, where a fixed ti

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Guo, Changliang, Duo Fang, Chengzong Wang, et al. "Ultrasonic flaw detection spectrogram characterization of vermicular graphite cast iron engine cylinder head." Journal of Physics: Conference Series 1996, no. 1 (2021): 012005. http://dx.doi.org/10.1088/1742-6596/1996/1/012005.

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Abstract The defects formed in the manufacture of the vermicular graphite cast iron engine cylinder head seriously affect the operation of the engine, which is necessary to detect. Ultrasonic testing is a non-destructive testing method that has the advantages of quick response, high resolution, and high security. In this paper, various types of specimens are prepared corresponding to different types of actual defects in the vermicular iron cylinder head. An ultrasonic A-scan system was built to test the specimens. The short-time Fourier transform, the continuous wavelet transform, the empirica

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García, Mario Alejandro, and Eduardo Atilio Destéfanis. "The Power Cepstrum Calculation with Convolutional Neural Networks." Journal of Computer Science and Technology 19, no. 2 (2019): e13. http://dx.doi.org/10.24215/16666038.19.e13.

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A model of neural network with convolutional layers that calculates the power cepstrum of the input signal is proposed. To achieve it, the network calculates the discrete-time short-term Fourier transform internally, obtaining the spectrogram of the signal as an intermediate step. The weights of the neural network can be calculated in a direct way or they can be obtained through training with the gradient descent method. The behaviour of the training is analysed. The model originally proposed cannot be trained in a complete way, but both the part that calculates the spectrogram and also a vari

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Ferraioli, Luigi, Michele Armano, Heather Audley, et al. "Kolmogorov-Smirnov like test for time-frequency Fourier spectrogram analysis in LISA Pathfinder." Experimental Astronomy 39, no. 1 (2014): 1–10. http://dx.doi.org/10.1007/s10686-014-9432-z.

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Gusev, V. G. "Double-exposure recording of a lensless fourier spectrogram for forming a shear interferogram." Russian Physics Journal 42, no. 5 (1999): 462–66. http://dx.doi.org/10.1007/bf02508218.

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Ahmed, Ammar, Youssef Serrestou, Kosai Raoof, and Jean-François Diouris. "Empirical Mode Decomposition-Based Feature Extraction for Environmental Sound Classification." Sensors 22, no. 20 (2022): 7717. http://dx.doi.org/10.3390/s22207717.

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In environment sound classification logs, Mel band energies (MBEs) are considered as the most successful and commonly used features for classification. The underlying algorithm, fast Fourier transform (FFT), is valid under certain restrictions. In this study, we address these limitations of Fourier transform and propose a new method to extract log Mel band energies using amplitude modulation and frequency modulation. We present a comparative study between traditionally used log Mel band energy features extracted by Fourier transform and log Mel band energy features extracted by our new approac

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Fakhrudin, Abdul Daffa, and Putu Harry Gunawan. "Arrhythmia Classification Using CNN-SVM from ECG Spectrogram Representation." Eduvest - Journal of Universal Studies 4, no. 12 (2024): 11245–54. https://doi.org/10.59188/eduvest.v4i12.49993.

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Arrhythmia, a critical subset of cardiovascular diseases and a leading cause of morbidity and mortality, is caused by irregular heartbeats that disrupt the normal rhythm of the heart. Detecting arrhythmias accurately is essential for timely diagnosis and treatment, which can be achieved through electrocardiogram (ECG) signals. This study presents a hybrid Convolutional Neural Network (CNN) and Support Vector Machine (SVM) model for arrhythmia classification, leveraging spectrogram representations of ECG signals. The CNN extracts spatial and temporal features from the spectrograms, while the SV

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Qi, Xin. "Synthesis and Characterization of Strong Polar Macroporous Resin Made from Cellulose." Advanced Materials Research 346 (September 2011): 743–50. http://dx.doi.org/10.4028/www.scientific.net/amr.346.743.

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The macroporous adsorption resin synthesized in this experiment uses cullulose as monomer, adipoyl dichlorid as crosslinker, and cyclohexane as porogenic agent, which three corsslink and polymerize each other, forming the porous skeletal structure. The cellulose processing procedure is as follow: prepare alkali cellulose; crosslink the cellulose (etherification); etherify the cellulose; and functionalize the cellulose. By assaying the perssad characterization of the macroporous resin obtained in this experiment with Fourier infrared spectrometer, we observe hydroxyl group and ester group in th

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Mi, Dan, and Lu Qin. "Classification System of National Music Rhythm Spectrogram Based on Biological Neural Network." Computational Intelligence and Neuroscience 2022 (October 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/2047576.

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National music is a treasure of Chinese traditional culture. It contains the cultural characteristics of various regions and reflects the core value of Chinese traditional culture. Classification technology classifies a large number of unorganized drama documents, which are not labeled, and to some extent, it helps folk music better enter the lives of ordinary people. Simulate folk music of different spectrum and record corresponding music audio under laboratory conditions Through Fourier transform and other methods, music audio is converted into spectrogram, and a total of 2608 two-dimensiona

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Hendriks, Jacob, and Patrick Dumond. "Exploring the Relationship between Preprocessing and Hyperparameter Tuning for Vibration-Based Machine Fault Diagnosis Using CNNs." Vibration 4, no. 2 (2021): 284–309. http://dx.doi.org/10.3390/vibration4020019.

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This paper demonstrates the differences between popular transformation-based input representations for vibration-based machine fault diagnosis. This paper highlights the dependency of different input representations on hyperparameter selection with the results of training different configurations of classical convolutional neural networks (CNNs) with three common benchmarking datasets. Raw temporal measurement, Fourier spectrum, envelope spectrum, and spectrogram input types are individually used to train CNNs. Many configurations of CNNs are trained, with variable input sizes, convolutional k

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Cherkashyn, Dmytro, Oleksii Saienko, and Serhii Hubskyi. "USE OF VIBRATION DIAGNOSTICS TO MONITOR AND CONTROL THE TECHNICAL CONDITION OF AUTOMOTIVE COMPONENTS." Bulletin of the National Technical University «KhPI». Series: Automobile and Tractor Construction, no. 2 (November 28, 2024): 97–107. http://dx.doi.org/10.20998/2078-6840.2024.2.10.

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The paper provides an analysis of vibrations arising during the operation of automotive components, which is an effective tool for diagnosing and monitoring the technical condition of automotive components. It allows timely detection of mechanical faults, such as imbalance, bearing wear, and damage to engine components. The use of the Fast Fourier Transform and spectrogram increases the accuracy of the analysis, especially in conditions of variable vibration frequencies, providing continuous monitoring and support of automotive systems.

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Eleyan, Alaa, Fatih Bayram, and Gülden Eleyan. "Spectrogram-Based Arrhythmia Classification Using Three-Channel Deep Learning Model with Feature Fusion." Applied Sciences 14, no. 21 (2024): 9936. http://dx.doi.org/10.3390/app14219936.

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This paper introduces a novel deep learning model for ECG signal classification using feature fusion. The proposed methodology transforms the ECG time series into a spectrogram image using a short-time Fourier transform (STFT). This spectrogram is further processed to generate a histogram of oriented gradients (HOG) and local binary pattern (LBP) features. Three separate 2D convolutional neural networks (CNNs) then analyze these three image representations in parallel. To enhance performance, the extracted features are concatenated before feeding them into a gated recurrent unit (GRU) model. T

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Lilensten, J., and P. O. Amblard. "Time-frequency tools of signal processing for EISCAT data analysis." Annales Geophysicae 14, no. 12 (1996): 1513–25. http://dx.doi.org/10.1007/s00585-996-1513-5.

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Abstract. We demonstrate the usefulness of some signal-processing tools for the EISCAT data analysis. These tools are somewhat less classical than the familiar periodogram, squared modulus of the Fourier transform, and therefore not as commonly used in our community. The first is a stationary analysis, "Thomson's estimate'' of the power spectrum. The other two belong to time-frequency analysis: the short-time Fourier transform with the spectrogram, and the wavelet analysis via the scalogram. Because of the highly non-stationary character of our geophysical signals, the latter two tools are bet

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Chang, Sukjoo, and Irine Gotsiridze. "STFT Spectrogram for Epilepsy Seizure Detection and Channel Selection." Works of Georgian Technical University, no. 2(536) (May 16, 2025): 120–26. https://doi.org/10.36073/1512-0996-2025-2-120-126.

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Epilepsy is the chronic neurological disorder which affects more than 1% of the population in the world. More than 50 million individuals have been suffered and living under the condition, among them, approximately 30% of patients were suffer from intractable epilepsy which is hard to control with convulsant medication. (Fisher et al, 2005). Various factors induce epilepsy such as trauma, postoperative brain damage, acquired condition, or genetic factors but nothing may not be confirmed for sure to its reason. The main problem and challenging factor of epilepsy is unprovoked seizures which are

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Ma, Longbo, Jianying Zheng, and Jianliang Zhao. "Flow fields disturbance research of regulating valve based on short time fourier transform spectrogram." JOURNAL OF ELECTRONIC MEASUREMENT AND INSTRUMENT 2009, no. 8 (2009): 72–77. http://dx.doi.org/10.3724/sp.j.1187.2009.08072.

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