Relevant bibliographies by topics / Fourier spectrogram

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Fourier spectrogram'

Author: Grafiati
Published: 19 February 2023
Last updated: 31 July 2025

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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 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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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, w
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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 o
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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 p
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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 étiq
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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 e
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Jemâa, Imen. "Suivi de formants par analyse en multirésolution." Electronic Thesis or Diss., Université de Lorraine, 2013. http://www.theses.fr/2013LORR0026.

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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 étiq
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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
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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<br>國立成功大學<br>航空太空工程學系碩博士班<br>96<br>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
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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<br>國立交通大學<br>電信工程研究所<br>102<br>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 th
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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
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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. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17478-0_4.

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Chakravarty, Nidhi, and Mohit Dua. "Securing Automatic Speaker Verification Systems Using Residual Networks." In Advances in Information Security, Privacy, and Ethics. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2223-9.ch005.

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Spoofing attacks are a major risk for automatic speaker verification systems, which are becoming more widespread. Adequate countermeasures are necessary since attacks like replay, synthetic, and deepfake attacks, are difficult to identify. Technologies that can identify audio-level attacks must be developed in order to address this issue. In this chapter, the authors have proposed combination of different spectrogram-based techniques with Residual Networks34 (ResNet34) for securing the automatic speaker verification (ASV) systems. The methodology uses Mel frequency scale-based Mel-spectrogram
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Arthy, J., K. Raja, Vignesh Raghuraman, S. Ashwath, and Rahul Bundele. "Avian Species Detection Using Spectogram and CNN." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-9208-9.ch009.

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Avian species detection using spectrogram and CNN showcases the method of converting bird sounds into spectrogram images, which in turn is used as input for the CNN models. This study would discuss how the CNNs are trained to find and differentiate bird species based on these spectrograms, highlighting the accuracy and efficiency of this approach.Python programming language and Librosa library are used for the implementation of the models.A web application is also developed to showcase the results of the model.As we are using STFT (Short Term Fourier Transforms) the accuracy of the model incre
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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 obta
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"Stationarity and Spectrograms." In A Primer on Fourier Analysis for the Geosciences. Cambridge University Press, 2019. http://dx.doi.org/10.1017/9781316543818.009.

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Govarthan, Praveen Kumar, Sriram Kumar P, Nagarajan Ganapathy, and Jac Fredo Agastinose Ronickom. "Deep Learning Framework for Categorical Emotional States Assessment Using Electrodermal Activity Signals." In Studies in Health Technology and Informatics. IOS Press, 2023. http://dx.doi.org/10.3233/shti230418.

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In this study, we attempted to classify categorical emotional states using Electrodermal Activity (EDA) signals and a configurable Convolutional Neural Network (cCNN). The EDA signals from the publicly available, Continuously Annotated Signals of Emotion dataset were down-sampled and decomposed into phasic components using the cvxEDA algorithm. The phasic component of EDA was subjected to Short-Time Fourier Transform-based time-frequency representation to obtain spectrograms. These spectrograms were input to the proposed cCNN to automatically learn the prominent features and discriminate varie
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Verma, Gunjan, Honey Gocher, Sweety Verma, Yudhveer Singh, Arti Kaushik, and Avinash Goswami. "A Comprehensive Review on Advancements and Challenges in Audio Classification Through Deep Learning." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-8009-3.ch007.

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Deep learning-based audio classification has transformed the industry with improved speech recognition, genre identification in music, and ambient sound detection. The article explores various approaches, including model architectures, evaluation metrics, and preprocessing techniques. Traditional methods are compared to deep learning techniques, which have enhanced performance. Spectrograms, Mel-Frequency Cepstral Coefficients, and Short-Time Fourier Transform are discussed as preprocessing techniques. The study also evaluates hybrid model architectures, training methods, data augmentation, an
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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. 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 esse
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Conference papers on the topic "Fourier spectrogram"

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Priya, M. Yagnasri, Sanjivani P. Shendre, and Peeta Basa Pati. "Enhanced Gunshot Sound Detection using AlexNet and XGBoost from Fourier Spectrograms." In 2024 4th International Conference on Intelligent Technologies (CONIT). IEEE, 2024. http://dx.doi.org/10.1109/conit61985.2024.10626951.

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Lei, Helen, Ravi Gadgil, Sandeep Kumar Amgothu, and Dulal Kar. "UAV Audio Detection and Identification Using Short-Time Fourier Transform Spectrograms with Deep Learning Models." In 2025 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2025. https://doi.org/10.1109/icuas65942.2025.11007784.

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Bannajak, Krittapat, Pornapa Artsang, Nirawit Kunanta, Thananphat Phonthongchanthuek, Supachai Awiphan, and Panomsak Meemon. "Enhancing noise suppression and signal detection in Fourier transform spectrograph using lock-in amplifier." In 2024 International Conference on Photonics Solutions (ICPS2024), edited by Apichai Bhatranand. SPIE, 2025. https://doi.org/10.1117/12.3058702.

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Chotiphun, Darapan, Badin Damrongsak, and Pornapa Artsang. "Correcting instrument shifts in solar spectroscopy with telluric lines: a study with an in-house Fourier transform spectrograph." In 2024 International Conference on Photonics Solutions (ICPS2024), edited by Apichai Bhatranand. SPIE, 2025. https://doi.org/10.1117/12.3058667.

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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-dimension
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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. 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 complicate
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