Academic literature on the topic 'Fourier spectrogram'
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Journal articles on the topic "Fourier spectrogram"
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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 ship wave patterns and apply time–frequency analysis to generate spectrograms for an idealised ship. We clarify the role of the linear dispersion relation and ship speed on the two linear components. We use a simple weakly nonlinear theory to identify higher-order effects in a spectrogram and, while the high-speed ferry data are very noisy, we propose that certain additional features in the experimental data are caused by nonlinearity. Finally, we provide a possible explanation for a further discrepancy between the high-speed ferry spectrograms and linear theory by accounting for ship acceleration.
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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 (July 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 (November 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 prediction model.
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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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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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Lu, Wenkai, and Fangyu Li. "Seismic spectral decomposition using deconvolutive short-time Fourier transform spectrogram." GEOPHYSICS 78, no. 2 (March 1, 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 seismic signal bandwidth only. In general, because the low-frequency components of a seismic signal are dominant, the removal of the negative frequencies may introduce a sharp edge at the zero frequency, which would produce artifacts in the DSTFT spectrogram. To avoid this problem, we used the analytic signal, which is obtained by applying the Hilbert transform on the original real seismic signal, to calculate the STFT spectrogram in our method. Synthetic and real seismic data examples were evaluated to demonstrate the performance of the proposed method.
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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 (December 23, 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 relates with the energy of the earthquake. The result including three parameters, they are time, frequency and the spectrogram. It makes easier for the geophysicist to analyse the frequency changing in each time based on the spectrogram colour. Besides that, it can be used to identify the arrival time of P and S wave as the important information in calculate the hypocentre location of the earthquake.
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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 (April 30, 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 .With the help of developed transform kernel , the magnitude-wise shift invariance property is verified and timefrequency content is analyzed by plotting spectrogram. Keywords: STFT, FrFT, kernel, Time-Bandwidth product, spectrogram.
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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 (December 7, 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. Moreover, spectrogram calculation is time-efficient through short-time Fourier transformation (STFT) which allowed to present reduced data with well-distinguishable form to convolutional neural network (CNN). The data reduction was performed through frequency filtration by taking a specific cutoff value. These steps makes architecture of the CNN model simple which showed high accuracy. The proposed approach reduced memory usage and computational power through not using complex CNN models. A large publicly available PTB-XL dataset was utilized, and two datasets were prepared, i.e., spectrograms and raw signals for binary classification. The highest accuracy of 99.06% was achieved by the proposed approach, which reflects spectrograms are better than the raw signals for ECG classification. Further, up- and down-sampling of the signals were also performed at various sampling rates and accuracies were attained.
Dissertations / Theses on the topic "Fourier spectrogram"
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Krejčí, Michal. "Fourierova transformace a spektrogramy v analýze DNA sekvencí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-219249.
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Various methods of DNA sequences modifications for frequency analysis and basic characteristics of DNA are described in the theoretical part of this thesis. Tricolor spectrograms, created by short time Fourier transform help us to recognize some characteristic patterns in DNA sequences. Practical part of this work deals with developed programme which generates spectrograms and analyse them. Last part deals with the analysis of selected sequences of C. elegans genome. Some patterns are related to data of public databases such as NCBI. Various patterns are explained from the biological nature, which relates to chromosome structure and protein coding regions. Another well recognised patterns, tandem repetitions composed of satellites, microsatellites and minisatelites are described by spectrograms as well.
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Postránecká, Tereza. "Porovnání metod pro konstrukci barevných DNA spektrogramů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2013. http://www.nusl.cz/ntk/nusl-220019.
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This thesis discusses about possibilities of construction colour DNA spectrograms and about patterns which can be detected there. Spectrograms as tools of spectral analysis give us a simultaneous view of the local frequency throughout the nucleotide sequence. They are suitable for gene identification or gene regions identification, determination of global character about whole chromosomes and also give us a chance for the discovery of yet unknown regions of potential significance. For purpose of this kind of DNA analysis is possible to use digital signal processing methods. We can apply them on only after conversion of DNA sequence to numerical representation. Selection of correct numerical representation affects how well will be reflected biological features in numerical record which we need for another use in digital signal analysis.
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Vidal, Rosemeire Cardozo. "Algoritmo para estimar gravidade de DPOC através de sinais acústicos." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/3/3152/tde-11072017-151033/.
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O presente estudo tem como objetivo determinar se a gravidade da DPOC poderá ser estimada através da área do gráfico das intensidades sonoras dos sons respiratórios de pacientes com DPOC. O estudo realizado com 51 pacientes com DPOC leve, moderado, grave, muito grave e 7 indivíduos saudáveis não fumantes. Os sons respiratórios de cada participante, foram coletados através de estetoscópio adaptado com um mini microfone. O método compara as áreas das intensidades sonoras em função da frequência de pacientes de DPOC e indivíduos saudáveis. Neste contexto, para atender ao objetivo, um método foi proposto e testado baseado na combinação de técnicas de filtragem e TFTC, seguida de análise estatística, cálculo da média, desvio padrão e interpolação. Os resultados sugerem que a área do gráfico da variância da intensidade sonora em função da frequência diminui quando aumenta a gravidade da DPOC, exceto para os casos em que a bronquite crônica é predominante.The present study aims to determine if the severity of COPD can be estimated through the chart area of the sound intensities of respiratory sounds in patients with COPD. The study included 51 patients with mild, moderate, severe, very severe COPD and 7 healthy non-smokers. The breathing sounds of each participant were collected through a stethoscope adapted with a mini microphone. The method compares the areas of intensity sonic densities as a function of the frequency of COPD patients and healthy individuals. In this context, to meet the objective, a method was proposed and tested based on the combination of filtering techniques and TFTC, followed by statistical analysis, calculation of the mean, standard deviation and interpolation. The results suggest that the area of the graph of frequency-frequency sound intensity variance decreases as the severity of COPD increases, except for cases where chronic bronchitis is predominant.
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Jemâa, Imen. "Suivi de formants par analyse en multirésolution." Thesis, Université de Lorraine, 2013. http://www.theses.fr/2013LORR0026/document.
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Nos travaux de recherches présentés dans ce manuscrit ont pour objectif, l'optimisation des performances des algorithmes de suivi des formants. Pour ce faire, nous avons commencé par l'analyse des différentes techniques existantes utilisées dans le suivi automatique des formants. Cette analyse nous a permis de constater que l'estimation automatique des formants reste délicate malgré l'emploi de diverses techniques complexes. Vue la non disponibilité des bases de données de référence en langue arabe, nous avons élaboré un corpus phonétiquement équilibré en langue arabe tout en élaborant un étiquetage manuel phonétique et formantique. Ensuite, nous avons présenté nos deux nouvelles approches de suivi de formants dont la première est basée sur l'estimation des crêtes de Fourier (maxima de spectrogramme) ou des crêtes d'ondelettes (maxima de scalogramme) en utilisant comme contrainte de suivi le calcul de centre de gravité de la combinaison des fréquences candidates pour chaque formant, tandis que la deuxième approche de suivi est basée sur la programmation dynamique combinée avec le filtrage de Kalman. Finalement, nous avons fait une étude exploratrice en utilisant notre corpus étiqueté manuellement comme référence pour évaluer quantitativement nos deux nouvelles approches par rapport à d'autres méthodes automatiques de suivi de formants. Nous avons testé la première approche par détection des crêtes ondelette, utilisant le calcul de centre de gravité, sur des signaux synthétiques ensuite sur des signaux réels de notre corpus étiqueté en testant trois types d'ondelettes complexes (CMOR, SHAN et FBSP). Suite à ces différents tests, il apparaît que le suivi de formants et la résolution des scalogrammes donnés par les ondelettes CMOR et FBSP sont meilleurs qu'avec l'ondelette SHAN. Afin d'évaluer quantitativement nos deux approches, nous avons calculé la différence moyenne absolue et l'écart type normalisée. Nous avons fait plusieurs tests avec différents locuteurs (masculins et féminins) sur les différentes voyelles longues et courtes et la parole continue en prenant les signaux étiquetés issus de la base élaborée comme référence. Les résultats de suivi ont été ensuite comparés à ceux de la méthode par crêtes de Fourier en utilisant le calcul de centre de gravité, de l'analyse LPC combinée à des bancs de filtres de Mustafa Kamran et de l'analyse LPC dans le logiciel Praat. D'après les résultats obtenus sur les voyelles /a/ et /A/, nous avons constaté que le suivi fait par la méthode ondelette avec CMOR est globalement meilleur que celui des autres méthodes Praat et Fourier. Cette méthode donne donc un suivi de formants (F1, F2 et F3) pertinent et plus proche de suivi référence. Les résultats des méthodes Fourier et ondelette sont très proches dans certains cas puisque toutes les deux présentent moins d'erreurs que la méthode Praat pour les cinq locuteurs masculins ce qui n'est pas le cas pour les autres voyelles où il y a des erreurs qui se présentent parfois sur F2 et parfois sur F3. D'après les résultats obtenus sur la parole continue, nous avons constaté que dans le cas des locuteurs masculins, les résultats des deux nouvelles approches sont notamment meilleurs que ceux de la méthode LPC de Mustafa Kamran et ceux de Praat même si elles présentent souvent quelques erreurs sur F3. Elles sont aussi très proches de la méthode par détection de crêtes de Fourier utilisant le calcul de centre de gravité. Les résultats obtenus dans le cas des locutrices féminins confirment la tendance observée sur les locuteursOur research work presented in this thesis aims the optimization of the performance of formant tracking algorithms. We began by analyzing different existing techniques used in the automatic formant tracking. This analysis showed that the automatic formant estimation remains difficult despite the use of complex techniques. For the non-availability of database as reference in Arabic, we have developed a phonetically balanced corpus in Arabic while developing a manual phonetic and formant tracking labeling. Then we presented our two new automatic formant tracking approaches which are based on the estimation of Fourier ridges (local maxima of spectrogram) or wavelet ridges (local maxima of scalogram) using as a tracking constraint the calculation of center of gravity of a set of candidate frequencies for each formant, while the second tracking approach is based on dynamic programming combined with Kalman filtering. Finally, we made an exploratory study using manually labeled corpus as a reference to quantify our two new approaches compared to other automatic formant tracking methods. We tested the first approach based on wavelet ridges detection, using the calculation of the center of gravity on synthetic signals and then on real signals issued from our database by testing three types of complex wavelets (CMOR, SHAN and FBSP). Following these tests, it appears that formant tracking and scalogram resolution given by CMOR and FBSP wavelets are better than the SHAN wavelet. To quantitatively evaluate our two approaches, we calculated the absolute difference average and standard deviation. We made several tests with different speakers (male and female) on various long and short vowels and continuous speech signals issued from our database using it as a reference. The formant tracking results are compared to those of Fourier ridges method calculating the center of gravity, LPC analysis combined with filter banks method of Kamran.M and LPC analysis integrated in Praat software. According to the results of the vowels / a / and / A /, we found that formant tracking by the method with wavelet CMOR is generally better than other methods. Therefore, this method provides a correct formant tracking (F1, F2 and F3) and closer to the reference. The results of Fourier and wavelet methods are very similar in some cases since both have fewer errors than the method Praat. These results are proven for the five male speakers which is not the case for the other vowels where there are some errors which are present sometimes in F2 and sometimes in F3. According to the results obtained on continuous speech, we found that in the case of male speakers, the result of both approaches are particularly better than those of Kamran.M method and those of Praat even if they are often few errors in F3. They are also very close to the Fourier ridges method using the calculation of center of gravity. The results obtained in the case of female speakers confirm the trend observed over the male speakers
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Movin, Andreas, and Jonathan Jilg. "Kan datorer höra fåglar?" Thesis, KTH, Skolan för teknikvetenskap (SCI), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254800.
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Ljudigenkänning möjliggörs genom spektralanalys, som beräknas av den snabba fouriertransformen (FFT), och har under senare år nått stora genombrott i samband med ökningen av datorprestanda och artificiell intelligens. Tekniken är nu allmänt förekommande, i synnerhet inom bioakustik för identifiering av djurarter, en viktig del av miljöövervakning. Det är fortfarande ett växande vetenskapsområde och särskilt igenkänning av fågelsång som återstår som en svårlöst utmaning. Även de främsta algoritmer i området är långt ifrån felfria. I detta kandidatexamensarbete implementerades och utvärderades enkla algoritmer för att para ihop ljud med en ljuddatabas. En filtreringsmetod utvecklades för att urskilja de karaktäristiska frekvenserna vid fem tidsramar som utgjorde basen för jämförelsen och proceduren för ihopparning. Ljuden som användes var förinspelad fågelsång (koltrast, näktergal, kråka och fiskmås) så väl som egeninspelad mänsklig röst (4 unga svenska män). Våra resultat visar att framgångsgraden normalt är 50–70%, den lägsta var fiskmåsen med 30% för en liten databas och den högsta var koltrasten med 90% för en stor databas. Rösterna var svårare för algoritmen att särskilja, men de hade överlag framgångsgrader mellan 50% och 80%. Dock gav en ökning av databasstorleken generellt inte en ökning av framgångsgraden. Sammanfattningsvis visar detta kandidatexamensarbete konceptbeviset bakom fågelsångigenkänning och illustrerar såväl styrkorna som bristerna av dessa enkla algoritmer som har utvecklats. Algoritmerna gav högre framgångsgrad än slumpen (25%) men det finns ändå utrymme för förbättring eftersom algoritmen vilseleddes av ljud av samma frekvenser. Ytterligare studier behövs för att bedöma den utvecklade algoritmens förmåga att identifiera ännu fler fåglar och röster.Sound recognition is made possible through spectral analysis, computed by the fast Fourier transform (FFT), and has in recent years made major breakthroughs along with the rise of computational power and artificial intelligence. The technology is now used ubiquitously and in particular in the field of bioacoustics for identification of animal species, an important task for wildlife monitoring. It is still a growing field of science and especially the recognition of bird song which remains a hard-solved challenge. Even state-of-the-art algorithms are far from error-free. In this thesis, simple algorithms to match sounds to a sound database were implemented and assessed. A filtering method was developed to pick out characteristic frequencies at five time frames which were the basis for comparison and the matching procedure. The sounds used were pre-recorded bird songs (blackbird, nightingale, crow and seagull) as well as human voices (4 young Swedish males) that we recorded. Our findings show success rates typically at 50–70%, the lowest being the seagull of 30% for a small database and the highest being the blackbird at 90% for a large database. The voices were more difficult for the algorithms to distinguish, but they still had an overall success rate between 50% and 80%. Furthermore, increasing the database size did not improve success rates in general. In conclusion, this thesis shows the proof of concept and illustrates both the strengths as well as short-comings of the simple algorithms developed. The algorithms gave better success rates than pure chance of 25% but there is room for improvement since the algorithms were easily misled by sounds of the same frequencies. Further research will be needed to assess the devised algorithms' ability to identify even more birds and voices.
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Snyder, Mark Alan. "Long-Term Ambient Noise Statistics in the Gulf of Mexico." ScholarWorks@UNO, 2007. http://scholarworks.uno.edu/td/595.
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Long-term omni-directional ambient noise was collected at several sites in the Gulf of Mexico during 2004 and 2005. The Naval Oceanographic Office deployed bottom moored Environmental Acoustic Recording System (EARS) buoys approximately 159 nautical miles south of Panama City, Florida, in water depths of 3200 meters. The hydrophone of each buoy was 265 meters above the bottom. The data duration ranged from 10-14 months. The buoys were located near a major shipping lane, with an estimated 1.5 to 4.5 ships per day passing nearby. The data were sampled at 2500 Hz and have a bandwidth of 10-1000 Hz. Data are processed in eight 1/3-octave frequency bands, centered from 25 to 950 Hz, and monthly values of the following statistical quantities are computed from the resulting eight time series of noise spectral level: mean, median, standard deviation, skewness, kurtosis and coherence time. Four hurricanes were recorded during the summer of 2004 and they have a major impact on all of the noise statistics. Noise levels at higher frequencies (400-950 Hz) peak during extremely windy months (summer hurricanes and winter storms). Standard deviation is least in the region 100-200 Hz but increases at higher frequencies, especially during periods of high wind variability (summer hurricanes). Skewness is positive from 25-400 Hz and negative from 630-950 Hz. Skewness and kurtosis are greatest near 100 Hz. Coherence time is low in shipping bands and high in weather bands, and it peaks during hurricanes. The noise coherence is also analyzed. The 14-month time series in each 1/3- octave band is highly correlated with other 1/3-octave band time series ranging from 2 octaves below to 2 octaves above the band's center frequency. Spatial coherence between hydrophones is also analyzed for hydrophone separations of 2.29, 2.56 and 4.84 km over a 10-month period. The noise field is highly coherent out to the maximum distance studied, 4.84 km. Additionally, fluctuations of each time series are analyzed to determine time scales of greatest variability. The 14-month data show clearly that variability occurs primarily over three time scales: 7-22 hours (shipping-related), 56-282 hours (2-12 days, weather-related) and over an 8-12 month period.
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Lee, Shang-Yin, and 李尚胤. "Qualitative Identification of SonocardiographySystem and Applications of Fourier SineSpectrum and Spectrogram." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/00262649069402849945.
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碩士國立成功大學
航空太空工程學系碩博士班
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The frequency response of the microphone of the Prof. F. M. Yu’s sonocardiography system is qualitatively proven to cover the 0.5 to 10 Hz zone. Experimental equipments include a function generator, small speaker,small microphone, audio board, and personal computer etc.. After the computer receives a signal from the microphone, we apply the Fourier sine spectrum/spectrogram generator to show the frequency response. The transformation involves the following steps. The non-sinusoidal part and extremely low-frequency part are removed by applying the iterative Gaussian smoothing method. In the remaining sinusoidal part, zero points around the two ends are identified by a searching procedure and interpolation. After dropping segments beyond the two zero ends, the corresponding Fourier sine spectrum is obtained by performing an odd function mapping. The time-frequency transform then imposes finite bandwidth Gaussian window upon the Fourier sine spectrum centered at a given frequency. The inverse Fourier transform of the band-pass limited spectrum gives the real part of the resulting spectrogram. The experimental results show that, in a small space, as long as the speaker has enough energy to push that small air cell, the microphone will receive low-frequency signal. According to the Wang’s frequency resonance theory of arterial and vascular system .We apply it to invest the pulse signal of a caesarean section including ABP and ECG signals.The corresponding spectrums and spectrograms show that every corresponding harmonic mode has amplitude and frequency variations with respect to time. We also compare the different operation point to explain its meaning with the spectrograms.
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Hsieh, Kun-Yeh, and 謝坤燁. "Multipitch Tracking by Factorial Hidden Markov Model Using Spectro-Temporal Modulations of Fourier Spectrogram." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/8d76n5.
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碩士國立交通大學
電信工程研究所
102
In recent years, pitch plays an important role in audio signal processing. Pitch tracking used in a wide range of applications. Single pitch tracking can make the error between the estimated pitch and true pitch within 5% in 90% frames, but there is a lot of room for improvement in multiple pitch tracking. In this thesis, we will apply Robust Algorithm Pitch Tracking (RAPT) to track the single speaker signal and to build up the prior probability and transition probability matrix of each speaker, and then we convert the spectrogram into rate-scale domain by the means which is inspired by cortical stage of auditory perceptual model. We use the value of rate-scale domain as feature vector and model the feature vector using Gaussian mixture models. Then we employ the mixture maximization model to establish the probability model for the feature vector of mixture speech. Finally, a FHMM is applied for tracking pitch over time. In the result of experiment, we found the system using rate-scale as feature vector has much capability of resisting noise than spectrum.
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Duan, Xiao. "The Fractional Fourier Transform and Its Application to Fault Signal Analysis." Thesis, 2012. http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-11207.
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To a large extent mathematical transforms are applied on a signal to uncover information that is concealed, and the capability of such transforms is valuable for signal processing. One such transforms widely used in this area, is the conventional Fourier Transform (FT), which decomposes a stationary signal into different frequency components. However, a major drawback of the conventional transform is that it does not easily render itself to the analysis of non-stationary signals such as a frequency modulated (FM) or amplitude modulated (AM) signal. The different frequency components of complex signals cannot be easily distinguished and separated from one another using the conventional FT. So in this thesis an innovative mathematical transform, Fractional Fourier Transform (FRFT), has been considered, which is more suitable to process non-stationary signals such as FM signals and has the capability not only of distinguishing different frequency components of a multi-component signal but also separating them in a proper domain, different than the traditional time or frequency domain.
The discrete-time FRFT (DFRFT) developed along with its derivatives, such as Multi-angle-DFRFT (MA-DFRFT), Slanted Spectrum and Spectrogram Based on Slanted Spectrum (SBSS) are tools belonging to the same FRFT family, and they could provide an effective approach to identify unknown signals and distinguish the different frequency components contained therein. Both artificial stationary and FM signals have been researched using the DFRFT and some derivative tools from the same family. Moreover, to accomplish a contrast with the traditional tools such as FFT and STFT, performance comparisons are shown to support the DFRFT as an effective tool in multi-component chirp signal analysis. The DFRFT taken at the optimum transform order on a single-component FM signal has provided higher degree of signal energy concentration compared to FFT results; and the Slanted Spectrum taken along the slant line obtained from the MA-DFRFT demonstration has shown much better discrimination between different frequency components of a multi-component FM signal.
As a practical application of these tools, the motor current signal has been analyzed using the DFRFT and other tools from FRFT family to detect the presence of a motor bearing fault and obtain the fault signature frequency. The conclusion drawn about the applicability of DFRFT and other derivative tools on AM signals with very slowly varying FM phenomena was not encouraging. Tools from the FRFT family appear more effective on FM signals, whereas AM signals are more effectively analyzed using traditional methods like spectrogram or its derivatives. Such methods are able to identify the signature frequency of faults while using less computational time and memory.
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(9781541), Steven Bleakley. "Time frequency analysis of railway wagon body accelerations for a low-power autonomous device." Thesis, 2006. https://figshare.com/articles/thesis/Time_frequency_analysis_of_railway_wagon_body_accelerations_for_a_low-power_autonomous_device/13436474.
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This thesis examines the application of the techniques of Fourier spectrogram and wavelet analysis to a low power embedded microprocessor application in a novel railway and rollingstock monitoring system. The safe and cost effective operation of freight railways is limited by the dynamic performance of wagons running on track. A monitoring system has been proposed comprising of low cost wireless sensing devices, dubbed "Health Cards", to be installed on every wagon in the fleet. When marshalled into a train, the devices would sense accelerations and communicate via radio network to a master system in the locomotive. The integrated system would provide online information for decision support systems. Data throughput was heavily restricted by the network architecture, so significant signal analysis was required at the device level. An electronics engineering team at Central Queensland University developed a prototype Health Card, incorporating a 27MHz microcontroller and four dual axis accelerometers. A sensing arrangement and online analysis algorithms were required to detect and categorise dynamic events while operating within the constraints of the system. Time-frequency analysis reveals the time varying frequency content of signals, making it suitable to detect and characterise transient events. With efficient algorithms such as the Fast Fourier Transform, and Fast Wavelet Transform, time-frequency analysis methods can be implemented on a low power, embedded microcontroller. This thesis examines the application of time-frequency analysis techniques to wagon body acceleration signals, for the purpose of detecting poor dynamic performance of the wagon-track system. The Fourier spectrogram is implemented on the Health Card prototype and demonstrated in the laboratory. The research and algorithms provide a foundation for ongoing development as resources become available for system testing and validation.
Book chapters on the topic "Fourier spectrogram"
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Fulop, Sean A. "The Fourier Power Spectrum and Spectrogram." In Signals and Communication Technology, 69–106. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17478-0_4.
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Haro, Marco, Mariko Nakano-Miyatake, Jorge Cime-Castillo, Humberto Lanz-Mendoza, Mario Gonzalez-Lee, and Hector Perez-Meana. "Infected Mosquito Detection System Using Spectral Analysis." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220296.
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Considering that an accurate detection of infected mosquitos may directly avoid the propagation of mosquito-borne disease; in this paper, we propose a detection system of infected mosquitos by Dengue virus type II, that uses seven spectral feature measures, which are applied to the spectrogram estimated from wingbeat signal emitted by mosquito’s flight. To evaluate the proposed system, we construct our own dataset with 20 infected Aedes aegypti by Dengue and 20 healthy ones. Seven spectral analysis methods, such as Spectral Rolloff, Spectral Centroide, etc., are applied to the spectrogram obtained by using the Short Time Fourier Transform (STFT) to generate feature vectors with 15 elements. These are feed into common machine learning techniques, such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Logistic Regression to detect the infected mosquitos differentiating form the healthy ones. Evaluation results show that, the best detection accuracy (84.32%) is provided by the KNN with K=3.
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"Stationarity and Spectrograms." In A Primer on Fourier Analysis for the Geosciences, 112–22. Cambridge University Press, 2019. http://dx.doi.org/10.1017/9781316543818.009.
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Ordóñez, Diego, Carlos Dafonte, Bernardino Arcay, and Minia Manteiga. "Connectionist Systems and Signal Processing Techniques Applied to the Parameterization of Stellar Spectra." In Soft Computing Methods for Practical Environment Solutions, 187–203. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-61520-893-7.ch012.
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A stellar spectrum is the finger-print identification of a particular star, the result of the radiation transport through its atmosphere. The physical conditions in the stellar atmosphere, its effective temperature, surface gravity, and the presence and abundance of chemical elements explain the observed features in the stellar spectra, such as the shape of the overall continuum and the presence and strength of particular lines and bands. The derivation of the atmospheric stellar parameters from a representative sample of stellar spectra collected by ground-based and spatial telescopes is essential when a realistic view of the Galaxy and its components is to be obtained. In the last decade, extensive astronomical surveys recording information of large portions of the sky have become a reality since the development of robotic or semi-automated telescopes. The Gaia satellite is one of the key missions of the European Space Agency (ESA) and its launch is planned for 2011. Gaia will carry out the so-called Galaxy Census by extracting precise information on the nature of its main constituents, including the spectra of objects (Wilkinson, 2005). Traditional methods for the extraction of the fundamental atmospheric stellar parameters (effective temperature (Teff), gravity (log G), metallicity ([Fe/H]), and abundance of alpha elements [a/Fe], elements integer multiples of the mass of the helium nucleus) are time-consuming and unapproachable for a massive survey involving 1 billion objects (about 1% of the Galaxy constituents) such as Gaia. This work presents the results of the authors’ study and shows the feasibility of an automated extraction of the previously mentioned stellar atmospheric parameters from near infrared spectra in the wavelength region of the Gaia Radial Velocity Spectrograph (RVS). The authors’ approach is based on a technique that has already been applied to problems of the non-linear parameterization of signals: artificial neural networks. It breaks ground in the consideration of transformed domains (Fourier and Wavelet Transforms) during the preprocessing stage of the spectral signals in order to select the frequency resolution that is best suited for each atmospheric parameter. The authors have also progressed in estimating the noise (SNR) that blurs the signal on the basis of its power spectrum and the application of noise-dependant algorithms of parameterization. This study has provided additional information that allows them to progress in the development of hybrid systems devoted to the automated classification of stellar spectra.
Conference papers on the topic "Fourier spectrogram"
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Qiang, Zhang, and Lu Wen‐kai. "Spectral decomposition using deconvolutive short time Fourier transform spectrogram." In SEG Technical Program Expanded Abstracts 2010. Society of Exploration Geophysicists, 2010. http://dx.doi.org/10.1190/1.3513143.
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Neammalai, Piyawat, Suphakant Phimoltares, and Chidchanok Lursinsap. "Speech and music classification using hybrid Form of spectrogram and fourier transformation." In 2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). IEEE, 2014. http://dx.doi.org/10.1109/apsipa.2014.7041658.
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Yan, Ruqiang, and Robert X. Gao. "Multi-Scale Enveloping Spectrogram for Bearing Defect Detection." In World Tribology Congress III. ASMEDC, 2005. http://dx.doi.org/10.1115/wtc2005-63541.
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This paper presents a new signal processing technique for bearing defect detection, called Multi-Scale Enveloping Spectrogram (MUSENS). The technique decomposes vibration signals measured on rolling bearings into different scales by means of a continuous wavelet transform (CWT). The envelope signal in each scale is then calculated from the modulus of the wavelet coefficients. Subsequently, Fourier transform is performed repetitively on the envelope of the signal at each scale, resulting in an “envelop spectrum” of the original signal at the various scales. The final output is a three-dimensional scale-frequency map that indicates the intensity and location of the defect-related frequency lines.
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Jeng, Yih-Nen, Tzung-Ming Yang, You-Chi Cheng, and Jia-Ming Huang. "Acoustic Data Analysis of Remote Control Vehicles via Fourier Sine Spectrum and Spectrogram." In 26th AIAA Aerodynamic Measurement Technology and Ground Testing Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2008. http://dx.doi.org/10.2514/6.2008-4263.
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Kaneko, Takuhiro, Kou Tanaka, Hirokazu Kameoka, and Shogo Seki. "ISTFTNET: Fast and Lightweight Mel-Spectrogram Vocoder Incorporating Inverse Short-Time Fourier Transform." In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9746713.
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MODIR, ALIREZA, and IBRAHIM TANSEL. "NEW EXCITATION (MULTIPLE WIDTH PULSE EXCITATION (MWPE)) METHOD FOR SHM SYSTEMS—PART 2: CLASSIFICATION OF TIME- FREQUENCY DOMAIN CHARACTERISTICS WITH 2DSSD AND CNN." In Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36345.
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Surface response to excitation (SuRE) and electromechanical impedance methods quantify the difference between the reference and any given spectrums by calculating the sum of the squares of differences (SSD). In part one of this study, twodimensional SSD (2D-SSD) was proposed to quantify the difference of timefrequency plots when the part was excited with the Multiple Width Pulse Excitation (MWPE) signal. In this study, neural networks and deep learning were used for the classification of structural health monitoring (SHM) signals. Since manual encoding of the 2D spectrograms is very complicated to prepare them for classification by using neural networks, deep learning has been used. In this study, the performance of deep learning was evaluated for the classification of sensory data. A cross-shaped part made of PLA was manufactured additively and the center of the part was excited with MWPE and the surface waves were monitored at the end of each extension. Tests were repeated without and with a compressive force at each extension. The recorded time-domain sensory data was converted to spectrogram images using Short-Time Fourier Transform (STFT). The spectrograms were classified with the Convolutional Neural Network (CNN) after proper training. The results showed that the hidden geometry of each extension had a distinctive effect on the characteristics of the monitored signals. CNN could classify the infill type, skin thickness, and loading conditions with better than 92 % accuracy when the responses of the 20 pulses in the MWPE signal were considered.
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Monteiro, Rodrigo, Carmelo Bastos-Filho, Mariela Cerrada, Diego Cabrera, and Rene-Vinicio Sanchez. "Convolutional Neural Networks Using Fourier Transform Spectrogram to Classify the Severity of Gear Tooth Breakage." In 2018 International Conference on Sensing,Diagnostics, Prognostics, and Control (SDPC). IEEE, 2018. http://dx.doi.org/10.1109/sdpc.2018.8664985.
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Parkhi, Abhinav, and Mahesh Pawar. "Analysis of Deformities in Lung Using Short Time Fourier Transform Spectrogram Analysis on Lung Sound." In 2011 International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2011. http://dx.doi.org/10.1109/cicn.2011.35.
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Mohammed, Sufyan A., Nouby M. Ghazaly, and Jamil Abdo. "Gearbox Vibration Analysis Using a Spectrogram and Power Spectrum Approach." In ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-95218.
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Abstract Vibration analysis is essential in rotating machinery fault diagnosis. As a vibration contains the dynamic information of a machine, improvement based on analysis has an effective role in predictive and preventive maintenance. In the present paper, the short Fourier transform is applied to determine the frequency variation of a gearbox signal with time due to different loads and driver speeds. In addition, the power spectral density (PSD) is used to represent the randomness of the signal since many frequencies occur simultaneously. The gearbox health condition is measured, and signal fault is simulated as tooth breakage for five cases: 0% (healthy), 25%, 50%, 75%, and 100% (complete tooth breakage). The obtained results proved that it is more powerful to use both spectrograms and PSD for gearbox fault diagnosis. This method is also improved with the ability to distinguish gearbox vibration signals for anomaly detection.
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Alsaif, Saif Abdulmohsen, and Tameem Saud Alothman. "Predict Drilling Equipment Failure Using AI-Based Sound Waive Analysis Methodology." In Offshore Technology Conference. OTC, 2022. http://dx.doi.org/10.4043/31828-ms.
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Abstract Using AI-Based sound wave analysis of drilling equipment to assess its health condition and to predict equipment failure by detecting anomalous sound patterns in both time and frequency domains, and apply predictive maintenance. Machines can produce noise with frequencies higher than the upper audible limit of human hearing, which is referred to as ultrasound (or ultrasonic sound). Thus, by listening to a wider sound spectrum, a better understanding of equipment state is achieved; and will lead to more accurate failure prediction methods. The process starts by capturing sounds via microphone. Then, Short Time Fourier Transform is used to convert time domain sound wave to its corresponding frequency domain. Both time and frequency domain signals are combined to form a two-dimensional spectrogram. This spectrogram is trained using an AI model to learn the difference between normal and abnormal equipment sounds. After training, the system identifies anomalous sounds the equipment might produce. Hence, generate an alert to the operational team to take actions, preventing equipment failure. Compared to the human auditory system, the experimental results showed that the proposed method achieved significant improvement in anomalous sounds detection only for machines that can produce noise in the ultrasound region, while other machines achieved worse results compared to the audible sound region.