Relevant bibliographies by topics / Spectrogram

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Spectrogram'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Spectrogram"

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Lee, Sang-Hoon, Hyun-Wook Yoon, Hyeong-Rae Noh, Ji-Hoon Kim, and Seong-Whan Lee. "Multi-SpectroGAN: High-Diversity and High-Fidelity Spectrogram Generation with Adversarial Style Combination for Speech Synthesis." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (2021): 13198–206. http://dx.doi.org/10.1609/aaai.v35i14.17559.

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While generative adversarial networks (GANs) based neural text-to-speech (TTS) systems have shown significant improvement in neural speech synthesis, there is no TTS system to learn to synthesize speech from text sequences with only adversarial feedback. Because adversarial feedback alone is not sufficient to train the generator, current models still require the reconstruction loss compared with the ground-truth and the generated mel-spectrogram directly. In this paper, we present Multi-SpectroGAN (MSG), which can train the multi-speaker model with only the adversarial feedback by conditioning
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Johnson, Alexander. "An integrated approach for teaching speech spectrogram analysis to engineering students." Journal of the Acoustical Society of America 152, no. 3 (2022): 1962–69. http://dx.doi.org/10.1121/10.0014172.

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Spectrogram analysis is a vital skill for learning speech acoustics. Spectrograms are necessary for visualizing cause-effect relationships between speech articulator movements and the resulting sound produced. However, many interpretation techniques needed to read spectrograms are counterintuitive to engineering students who have been taught to use more rigid mathematical formulas. As a result, spectrogram reading is often challenging for these students who do not have prior background in acoustic phonetics. In this paper, a structured, inclusive framework for teaching spectrogram reading to s
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Kim, Seong-Yoon, Hyun-Min Lee, Chae-Young Lim, and Hyun-Woo Kim. "Detection of Abnormal Symptoms Using Acoustic-Spectrogram-Based Deep Learning." Applied Sciences 15, no. 9 (2025): 4679. https://doi.org/10.3390/app15094679.

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Acoustic data inherently contain a variety of information, including indicators of abnormal symptoms. In this study, we propose a method for detecting abnormal symptoms by converting acoustic data into spectrogram representations and applying a deep learning model. Spectrograms effectively capture the temporal and frequency characteristics of acoustic signals. In this work, we extract key features such as spectrograms, Mel-spectrograms, and MFCCs from raw acoustic data and use them as input for training a convolutional neural network. The proposed model is based on a custom ResNet architecture
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Basak, Gopal K., and Tridibesh Dutta. "Statistical Speaker Identification Based on Spectrogram Imaging." Calcutta Statistical Association Bulletin 59, no. 3-4 (2007): 253–63. http://dx.doi.org/10.1177/0008068320070309.

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Abstract: The paper addresses the problem of speaker identification based on spectrograms in the text dependent case. Using spectrogram segmentation, this paper, mainly, focusses on understanding the complex patterns in frequency and amplitude in an utterance of a given word by an individual. The features used for identifying a speaker based on an observed variable extracted from the spectrograms, rely on the distinct speaker effect, his/her interaction effect with the particular word and with the frequency bands of the spectrogram. Performance of this novel approach on spectrogram samples, co
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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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Shingchern D. You, Kai-Rong Lin, and Chien-Hung Liu. "Estimating Classification Accuracy for Unlabeled Datasets Based on Block Scaling." International Journal of Engineering and Technology Innovation 13, no. 4 (2023): 313–27. http://dx.doi.org/10.46604/ijeti.2023.11975.

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This paper proposes an approach called block scaling quality (BSQ) for estimating the prediction accuracy of a deep network model. The basic operation perturbs the input spectrogram by multiplying all values within a block by , where is equal to 0 in the experiments. The ratio of perturbed spectrograms that have different prediction labels than the original spectrogram to the total number of perturbed spectrograms indicates how much of the spectrogram is crucial for the prediction. Thus, this ratio is inversely correlated with the accuracy of the dataset. The BSQ approach demonstrates satisfac
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Li, Hong Ping, and Hong Li. "Establish an Artificial Neural Networks Model to Make Quantitative Analysis about the Capillary Electrophoresis Spectrum." Advanced Materials Research 452-453 (January 2012): 1116–20. http://dx.doi.org/10.4028/www.scientific.net/amr.452-453.1116.

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Simulating the overlapping capillary electrophoresis spectrogram under the dissimilar conditions by the computer system , Choosing the overlapping capillary electrophoresis spectrogram simulated under the different conditions , processing the data to compose a neural network training regulations, Applying the artificial neural networks method to make a quantitative analysis about the multi-component in the overlapping capillary electrophoresis spectrogram,Using: Radial direction primary function neural network model and multi-layered perceptron neural network model. The findings indicated that
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Li, Juan, Xueying Zhang, Lixia Huang, Fenglian Li, Shufei Duan, and Ying Sun. "Speech Emotion Recognition Using a Dual-Channel Complementary Spectrogram and the CNN-SSAE Neutral Network." Applied Sciences 12, no. 19 (2022): 9518. http://dx.doi.org/10.3390/app12199518.

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In the background of artificial intelligence, the realization of smooth communication between people and machines has become the goal pursued by people. Mel spectrograms is a common method used in speech emotion recognition, focusing on the low-frequency part of speech. In contrast, the inverse Mel (IMel) spectrogram, which focuses on the high-frequency part, is proposed to comprehensively analyze emotions. Because the convolutional neural network-stacked sparse autoencoder (CNN-SSAE) can extract deep optimized features, the Mel-IMel dual-channel complementary structure is proposed. In the fir
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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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Godbole, Shubham, Vaishnavi Jadhav, and Gajanan Birajdar. "Indian Language Identification using Deep Learning." ITM Web of Conferences 32 (2020): 01010. http://dx.doi.org/10.1051/itmconf/20203201010.

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Spoken language is the most regular method of correspondence in this day and age. Endeavours to create language recognizable proof frameworks for Indian dialects have been very restricted because of the issue of speaker accessibility and language readability. However, the necessity of SLID is expanding for common and safeguard applications day by day. Feature extraction is a basic and important procedure performed in LID. A sound example is changed over into a spectrogram visual portrayal which describes a range of frequencies in regard with time. Three such spectrogram visuals were generated
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Dissertations / Theses on the topic "Spectrogram"

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Lampert, Thomas. "Spectrogram track detection : an active contour algorithm." Thesis, University of York, 2010. http://etheses.whiterose.ac.uk/956/.

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In many areas of science, near-periodic phenomena represent important information within time-series data. This thesis takes the example of the detection of non-transitory frequency components in passive sonar data, a problem which finds many applications. This problem is typically transformed into the pattern recognition domain by representing the time-series data as a spectrogram, in which slowly varying periodic signals appear as curvilinear tracks. The research is initiated with a survey of the literature, which is focused upon research into the detection of tracks within spectrograms. An
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Behr, Michael K. "State-space multitaper spectrogram algorithms : theory and applications." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/107033.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 63-67).<br>I present the state-space multitaper approach for analyzing non-stationary time series. Nonstationary time series are commonly divided into small time windows for analysis, but existing methods lose predictive pow
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Hansen, Vedal Amund. "Unsupervised Audio Spectrogram Compression using Vector Quantized Autoencoders." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264947.

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Despite the recent successes of neural networks in a variety of domains, musical audio modeling is still considered a hard task, with features typically spanning tens of thousands of dimensions in input space. By formulating audio data compression as an unsupervised learning task, this project investigates the applicability of vector quantized neural network autoencoders for compressing spectrograms – image-like representations of audio. Using a recently proposed gradient-based method for approximating waveforms from reconstructed (real-valued) spectrograms, the discrete pipeline produces list
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Kotte, Timo Oliver. "Application of Image Processing Techniques for Lamb Wave Characterization." Thesis, Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/4787.

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Characterization of dispersion curves in plate-like structures is possible with guided Lamb waves. In this research, experimental development of dispersion curves relies on the spectrogram, which suffers from the Heisenberg Uncertainty Principle. Reassignment is capable of localizing ill--defined dispersion curves. Unfortunately, reassignment also introduces spurious components, which reduce reassignment performance. This research develops an algorithm that provides both localization of dispersion curves and elimination of spurious components. To achieve this, an alternative formulation of rea
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Lamel, Lori Faith. "Formalizing knowledge used in spectrogram reading : acoustic and perceptual evidence from stops." Thesis, Massachusetts Institute of Technology, 1988. http://hdl.handle.net/1721.1/14780.

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Bleakley, Steven Shea, and steven bleakley@qr com au. "Time Frequency Analysis of Railway Wagon Body Accelerations for a Low-Power Autonomous Device." Central Queensland University, 2006. http://library-resources.cqu.edu.au./thesis/adt-QCQU/public/adt-QCQU20070622.121515.

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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
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Tsiappoutas, Kyriakos Michael. "Byzantine Music Intervals: An Experimental Signal Processing Approach." ScholarWorks@UNO, 2004. http://scholarworks.uno.edu/td/470.

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We used a Byzantine Music piece performed by a well recognized chanter in order to derive experimentally the mean frequencies of the first five tones (D – A) of the diatonic scale of Byzantine Music. Then we compared the experimentally derived frequencies with frequencies proposed by two theoretical scales, both representative of traditional Byzantine Music chanting. We found that if a scale is performed by a traditional chanter is very close in frequency to the frequencies proposed theoretically. We then determined an allowed frequency deviation from the mean frequencies for each tone. The co
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Dias, Fábio Felix. "Uma estratégia para análise visual de Paisagens Acústicas com base em seleção de características discriminantes." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-29102018-172305/.

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O crescimento do volume de dados ocasionado pelo desenvolvimento tecnológico atual, tem sido fortemente evocado como premissa para a utilização de técnicas que auxiliem a exploração, análise e entendimento desses dados. Um conjunto dessas técnicas está compreendido na área de Visualização de Dados, que proporciona maneiras visuais de identificar padrões e tendências, além de extrair características obscuras dos dados. Tais abordagens podem ser aplicadas a qualquer problema que culmine na análise de dados. Um desses problemas é a utilização do som como ferramenta para descrever as característic
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Kalm, Helen. "Acoustic Soil-Rock Probing : A Case Study in Gubbängen." Thesis, KTH, Jord- och bergmekanik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-256081.

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Soil-rock probing (Jb-probing) is the most common probing method in Sweden. Due to the penetration capacity of the Jb-probing it can be performed in both soil and rock. However, the capacity also results in inherent limitations and uncertainties, such as the difficulty identifying the soil layer sequences of soft soils. In order to attain a more detailed soil layer sequence it is necessary to perform complementary probing and sampling methods, an inefficient and consequently costly procedure. By instead implementing non-interfering complementary methods performed simultaneously as the Jb-probi
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Lacko, Tomáš. "Akustická simulace jedoucího automobilu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-218916.

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This project includes an overview of different processing methods phonograms. It focuses primarily on time - frequency analysis carried out using Fast Fourier Transform (STFT). The main essence is to evaluate the time - frequency analysis of recorded motor vehicle, driving at different options, together with an analysis of driver activity in these variants. Based on the results of the analysis deals with the creation of synthetic audio signals of motor vehicle records. Further addresses the creation of the program for acoustic simulation engine is running smoothly when driving a car. Processin
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Books on the topic "Spectrogram"

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Martin, Ann. VOICE - a spectrogram computer display package. Woods Hole Oceanographic Institution, 1990.

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Elliott, K. H. Spectrograph user guide. School of Physics and Space Research, University of Birmingham, 1992.

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Robinson, R. D. The RGO spectrograph. 2nd ed. Anglo-Australian Observatory, 1985.

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D, Tsintikidis, and United States. National Aeronautics and Space Administration., eds. New voyager radio spectrograms of Uranus. Dept. of Physics and Astronomy, University of Iowa, 1990.

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Becher, Jacob. The simulated space proton environment for radiation effects on Space Telescope Imaging Spectrograph (STIS). Old Dominion University Research Foundation, 1992.

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H, Moseley S., and United States. National Aeronautics and Space Administration, eds. The Submillimeter And Far Infrared Experiment (SAFIRE): A PI class instrument for SOFIA. NASA, 1999.

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United States. National Aeronautics and Space Administration., ed. Aries X-ray objective grating spectrograph: Final report. Lockheed Palo Alto Research Laboratory, 1991.

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United States. National Aeronautics and Space Administration., ed. Aries X-ray objective grating spectrograph: Final report. Lockheed Palo Alto Research Laboratory, 1991.

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United States. National Aeronautics and Space Administration., ed. Aries X-ray objective grating spectrograph: Final report. Lockheed Palo Alto Research Laboratory, 1991.

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United States. National Aeronautics and Space Administration., ed. Goddard high resolution spectrograph instrument handbook. 2nd ed. Space Telescope Science Institute, 1990.

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Book chapters on the topic "Spectrogram"

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Kobrina, Anastasiya. "Sound Spectrogram." In Encyclopedia of Animal Cognition and Behavior. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-47829-6_613-1.

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Kobrina, Anastasiya. "Sound Spectrogram." In Encyclopedia of Animal Cognition and Behavior. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-55065-7_613.

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Fulop, Sean A. "The Reassigned Spectrogram." In Signals and Communication Technology. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17478-0_6.

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Riley, Michael D. "The Schematic Spectrogram." In The Kluwer International Series in Engineering and Computer Science. Springer US, 1989. http://dx.doi.org/10.1007/978-1-4613-1079-2_4.

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Montalvo, Ana, Yandre M. G. Costa, and José Ramón Calvo. "Language Identification Using Spectrogram Texture." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25751-8_65.

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Lorenzo, Javier, and Mario Hernández. "Habituation Based on Spectrogram Analysis." In Advances in Artificial Intelligence — IBERAMIA 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36131-6_91.

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Sawada, Shun, Yoshinari Takegawa, and Keiji Hirata. "On Hierarchical Clustering of Spectrogram." In Music Technology with Swing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01692-0_16.

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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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Lampert, Thomas A., Simon E. M. O’Keefe, and Nick E. Pears. "Line Detection Methods for Spectrogram Images." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-93905-4_16.

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Magazine, Raghav, Ayush Agarwal, Anand Hedge, and S. R. Mahadeva Prasanna. "Fake Speech Detection Using Modulation Spectrogram." In Speech and Computer. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20980-2_39.

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Conference papers on the topic "Spectrogram"

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Islam, Rumana, and Mohammed Tarique. "Spectrogram and Mel-Spectrogram Based Dysphonic Voice Detection Using Convolutional Neural Network." In 2024 International Conference on Electrical, Computer and Energy Technologies (ICECET). IEEE, 2024. http://dx.doi.org/10.1109/icecet61485.2024.10698112.

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Naman, Anugunj, and Gaibo Zhang. "FAST: Fast Audio Spectrogram Transformer." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889238.

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TV, Rithwik Vallabhan, Noel Martin, Remya Ramesh, Jaimy Jaimes, Gautham VG, and Gouthamkrishna R. "Seismic Spectrogram based Elephant Detection System." In 2024 11th International Conference on Advances in Computing and Communications (ICACC). IEEE, 2024. https://doi.org/10.1109/icacc63692.2024.10845525.

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Krémé, Marina, and Adrien Meynard. "Consistent Spectrogram Separation from Nonstationary Mixture." In 2024 32nd European Signal Processing Conference (EUSIPCO). IEEE, 2024. http://dx.doi.org/10.23919/eusipco63174.2024.10715175.

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Muktadi, Md Taisirul, Tanzeem Tahmeed Reza, Mohammed Tawshif Hossain, Eshat Jahan Arifa, and Md Fazlul Karim Khondakar. "Spectrogram-driven Emotion Detection from Electroencephalogram." In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE, 2025. https://doi.org/10.1109/ecce64574.2025.11013815.

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Tougui, Ilias, Mehdi Zakroum, Ouassim Karrakchou, and Mounir Ghogho. "PD-VOST: Parkinson’s Disease Voice Spectrogram Transformer." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889820.

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Arabshahi, Payman, Robert J. Marks, and Les E. Atlas. "Fully parallel, real-time optical architectures for superior time–frequency representations of signals." In OSA Annual Meeting. Optica Publishing Group, 1990. http://dx.doi.org/10.1364/oam.1990.thk7.

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A number of optical processors have been proposed for the generation of time–frequency representations of signals, such as the ambiguity function, spectrogram, and the Wigner distribution. Generalized time-frequency representations (GTFR's) have been proposed by Cohen.1 Spectrograms, Wigner distributions, the Choi–Williams distribution, and the Zhao-Atlas-Marks (ZAM) GTFR2 are subsumed as special cases. We show that Cohen's class of GTFRs can be realized optically by the straightforward augmentation of some established optical architectures by means of the simple operation of apodization. A ne
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Reyes-Gomez, Manuel J., Nebojsa Jojic, and Daniel P. W. Ellis. "Modelling Sound Dynamics Using Deformable Spectrograms: Segmenting the Spectrogram into Smooth Regions." In 2006 Fortieth Asilomar Conference on Signals, Systems and Computers. IEEE, 2006. http://dx.doi.org/10.1109/acssc.2006.356581.

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Edwards, Matthew E. "Computer-Replicated Spectrograms By Ensemble Averaging and Normalization." In Laser Applications to Chemical Analysis. Optica Publishing Group, 1992. http://dx.doi.org/10.1364/laca.1992.pd12.

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High resolution absorption bands, as produced by a tunable diode laser, are superimposed on a background that reflects both the variation of the laser-output power with frequency and the specificity of the spectrometer used to filter out unwanted cavity modes. Gross distortions of the absorption bands from these combined effects make the extraction of characteristic line data extremely tedious or error prone. To address these features, I measured high resolution infrared spectra in the 8.7 microns region using a tunable diode lasers spectrometer where the latter is interfaced -using the Labwin
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Flandrin, Patrick. "On spectrogram local maxima." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952903.

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Reports on the topic "Spectrogram"

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Joggerst, Candace Church. PDV spectrogram automated extractions. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1561071.

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Skurikhin, Alexei N., and Richard J. Stead. Seismic Spectrogram Recognition by Matching the Energy Distributions. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1331246.

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Lamei, Lori F. Formalizing Knowledge Used in Spectrogram Reading: Acoustic and Perceptual Evidence from Stops. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada206826.

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Sylvia, J. M., J. W. Haas, K. M. Spencer, et al. Field Raman Spectrograph for Environmental Analysis. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/3969.

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Rehm, K. E., and C. Bolduc. Upgrade of the area II spectrograph. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/166380.

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Brake, M. L. Vacuum Spectrograph for E-Beam Ablation Studies. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada190531.

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Aldering, Greg. Comparison of STIS and SNAP spectrograph throughputs. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/842491.

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H. FUNSTEN. IMAGING TIME-OF-FLIGHT ION MASS SPECTROGRAPH. Office of Scientific and Technical Information (OSTI), 2000. http://dx.doi.org/10.2172/768176.

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Stratton, B. C., R. J. Fonck, K. Ida, K. P. Jaehnig, and A. T. Ramsey. SPRED spectrograph upgrade: high resolution grating and improved absolute calibrations. Office of Scientific and Technical Information (OSTI), 1986. http://dx.doi.org/10.2172/5628855.

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Carrabba, M. M. Fiber optic Raman spectrograph for in situ environmental monitoring. Final report. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/10179341.

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