Relevant bibliographies by topics / Spectrograms

Contents

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

Academic literature on the topic 'Spectrograms'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Spectrograms"

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He, Yuan, Xinyu Li, Runlong Li, Jianping Wang, and Xiaojun Jing. "A Deep-Learning Method for Radar Micro-Doppler Spectrogram Restoration." Sensors 20, no. 17 (2020): 5007. http://dx.doi.org/10.3390/s20175007.

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Radio frequency interference, which makes it difficult to produce high-quality radar spectrograms, is a major issue for micro-Doppler-based human activity recognition (HAR). In this paper, we propose a deep-learning-based method to detect and cut out the interference in spectrograms. Then, we restore the spectrograms in the cut-out region. First, a fully convolutional neural network (FCN) is employed to detect and remove the interference. Then, a coarse-to-fine generative adversarial network (GAN) is proposed to restore the part of the spectrogram that is affected by the interferences. The sim
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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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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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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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Jiashen, Li, and Zhang Xianwu. "Extracting speech spectrogram of speech signal based on generalized S-transform." PLOS ONE 20, no. 1 (2025): e0317362. https://doi.org/10.1371/journal.pone.0317362.

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In speech signal processing, time-frequency analysis is commonly employed to extract the spectrogram of speech signals. While many algorithms exist to achieve this with high-quality results, they often lack the flexibility to adjust the resolution of the extracted spectrograms. However, applications such as speech recognition and speech separation frequently require spectrograms of varying resolutions. The flexibility of an algorithm in providing different resolutions is crucial for these applications. This paper introduces the generalized S-transform, and explains its fundamental theory and a
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Franzoni, Valentina. "Cross-domain synergy: Leveraging image processing techniques for enhanced sound classification through spectrogram analysis using CNNs." Journal of Autonomous Intelligence 6, no. 3 (2023): 678. http://dx.doi.org/10.32629/jai.v6i3.678.

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<p>In this paper, the innovative approach to sound classification by exploiting the potential of image processing techniques applied to spectrogram representations of audio signals is reviewed. This study shows the effectiveness of incorporating well-established image processing methodologies, such as filtering, segmentation, and pattern recognition, to enhance the feature extraction and classification performance of audio signals when transformed into spectrograms. An overview is provided of the mathematical methods shared by both image and spectrogram-based audio processing, focusing o
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Griffin, R. F., та R. E. M. Griffin. "The BE II λ 3130 Å Region in the Spectra of Vega and Sirius". Symposium - International Astronomical Union 111 (1985): 439. http://dx.doi.org/10.1017/s007418090007916x.

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Spectrograms of very high quality have been obtained of Vega and Sirius with the Mount Wilson 100-inch (2.5-m) telescope and coudé spectrograph. Examples of these plates, showing the Be II region in the ultraviolet, are exhibited. The reciprocal dispersion is 0.83 Å/mm (83 nm/m) and the FWHM is about 19 mÅ (1.9 pm). The spectrograms have a trailed width of 3 mm and are on IIa or IIIa emulsions.
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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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Dissertations / Theses on the topic "Spectrograms"

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Im, Jinhyeok. "Numerical analysis of spectrograms in attosecond photoionisation." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASP175.

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Depuis leur première observation au début des années 2000, les impulsions lumineuses attosecondes (1 as = 10^-18 s) dans le domaine de l'ultraviolet extrême (XUV) ont révolutionné l'étude de la dynamique électronique dans les atomes et les molécules. La spectroscopie attoseconde basée sur la photoionisation assistée par laser a permis d'observer des processus ultrarapides tels que les délais temporels dans l'effet photoélectrique. Cette approche consiste à mesurer l'énergie cinétique des photoélectrons émis lors de l'ionisation d'atomes ou de molécules par une impulsion attoseconde combinée à
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Ford, George Harold. "Spoken Language Identification from Processing and Pattern Analysis of Spectrograms." NSUWorks, 2014. http://nsuworks.nova.edu/gscis_etd/152.

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Prior speech and linguistics research has focused on the use of phonemes recognition in speech, and their use in formulation of recognizable words, to determine language identification. Some languages have additional phoneme sounds, which can help identify a language; however, most of the phonemes are common to a wide variety of languages. Legacy approaches recognize strings of phonemes as syllables, used by dictionary queries to see if a word can be found to uniquely identify a language. This dissertation research considers an alternative means of determining language identification of speech
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McMullen, Cheyenne. "ASMR, Spectrograms, and Adam Young: Shaping a Genre Through Frequencies." OpenSIUC, 2021. https://opensiuc.lib.siu.edu/theses/2839.

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Spectrogram analysis of the most popular works of Adam Young’s four major projects; Owl City, Sky Sailing, Port Blue, and The Score Project. Spectrograms reveal several elements separate to what waveforms can show, and better show elements like frequency saturation, frequency ranges, overtones, and timbral sections. All these elements also can be used to better describe the phenomenon of the Autonomous Sensory Meridian Response (ASMR) and can also be seen on a spectrogram. Because of the ability to see these different elements, a spectrogram provides a good vehicle to analyze and compare eleme
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Al-Darkazali, Mohammed. "Image processing methods to segment speech spectrograms for word level recognition." Thesis, University of Sussex, 2017. http://sro.sussex.ac.uk/id/eprint/71675/.

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The ultimate goal of automatic speech recognition (ASR) research is to allow a computer to recognize speech in real-time, with full accuracy, independent of vocabulary size, noise, speaker characteristics or accent. Today, systems are trained to learn an individual speaker's voice and larger vocabularies statistically, but accuracy is not ideal. A small gap between actual speech and acoustic speech representation in the statistical mapping causes a failure to produce a match of the acoustic speech signals by Hidden Markov Model (HMM) methods and consequently leads to classification errors. Cer
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Bergström, Joakim. "Transfer Learning on Ultrasound Spectrograms of Weld Joints for Predictive Maintenance." Thesis, Uppsala universitet, Signaler och system, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-424726.

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A big hurdle for many companies to start using machine learning is that trending techniques need a huge amount of structured data. One potential way to reduce the need for data is taking advantage of previous knowledge from a related task. This is so called transfer learning. A basic description of it would be when you take a model trained on existing data and reuse that for another problem. The purpose of this master thesis is to investigate if transfer learning can reduce the need for data when faced with a new machine learning task which is, in particular, to use transfer learning on ultras
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TAECHAWATTANANANT, PASRAWIN. "Peak identification and quantification in proteomic mass spectrograms using non-negative matrix factorization." Doctoral thesis, Kyoto University, 2020. http://hdl.handle.net/2433/253496.

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京都大学<br>0048<br>新制・課程博士<br>博士(薬科学)<br>甲第22651号<br>薬科博第123号<br>新制||薬科||13(附属図書館)<br>京都大学大学院薬学研究科薬科学専攻<br>(主査)教授 石濱 泰, 教授 緒方 博之, 教授 馬見塚 拓, 教授 山下 富義<br>学位規則第4条第1項該当<br>Doctor of Pharmaceutical Sciences<br>Kyoto University<br>DFAM
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Truskinger, Anthony M. "Semi-automated annotation of environmental acoustic recordings." Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/84543/8/Anthony_Truskinger_Thesis.pdf.

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Environmental sensors collect massive amounts of audio data. This thesis investigates computational methods to support human analysts in identifying faunal vocalisations from that audio. A series of experiments was conducted to trial the effectiveness of novel user interfaces. This research examines the rapid scanning of spectrograms, decision support tools for users, and cleaning methods for folksonomies. Together, these investigations demonstrate that providing computational support to human analysts increases their efficiency and accuracy; this allows bioacoustics projects to efficiently ut
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Carvalho, Kelly Cristiane Henschel Pobbe de. "Descrição fonético-acústica das vibrantes no português e no espanhol /." Assis : [s.n.], 2004. http://hdl.handle.net/11449/102467.

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Orientador: Rafael Eugenio Hoyos-Andrade<br>Banca: Adelaide Hercília P. Silva<br>Banca: Gisele Domingos do Mar<br>Banca: Mirian Therezinha da Matta Machado<br>Banca: Zilda Maria Zapparoli Castro Melo<br>Resumo: Neste trabalho observamos e contrastamos as realizações das consoantes chamadas vibrantes, no português e no espanhol, em diferentes contextos fônicos, do ponto de vista acústico. Para tanto, utilizamos o Multi-Speech, programa de análise de fala para Windows, produzido pela Kay Elemetrics, que possibilita o desenvolvimento das análises espectrográficas necessárias neste tipo de investi
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Carvalho, Kelly Cristiane Henschel Pobbe de [UNESP]. "Descrição fonético-acústica das vibrantes no português e no espanhol." Universidade Estadual Paulista (UNESP), 2004. http://hdl.handle.net/11449/102467.

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Made available in DSpace on 2014-06-11T19:32:09Z (GMT). No. of bitstreams: 0 Previous issue date: 2004-03-02Bitstream added on 2014-06-13T20:42:48Z : No. of bitstreams: 1 carvalho_kchp_dr_assis.pdf: 17423514 bytes, checksum: 5a2ddc3e71ca7722c0a452d6131d37ce (MD5)<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)<br>Neste trabalho observamos e contrastamos as realizações das consoantes chamadas vibrantes, no português e no espanhol, em diferentes contextos fônicos, do ponto de vista acústico. Para tanto, utilizamos o Multi-Speech, programa de análise de fala para Windows,
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Marques, Mário Dinis. "Aspetos de interpretação na música para saxofone na obra de Daniel Schnyder." Doctoral thesis, Universidade de Évora, 2013. http://hdl.handle.net/10174/12225.

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Refém de uma dicotomia na forma de tocar, o saxofone tem vindo desde a sua criação a percorrer um caminho de convergência num consequente estreitar de diferentes abordagens técnicas e interpretativas que definem quer a escola clássica quer o jazz. Com a multiplicação de géneros musicais e uma prolífera produção de compositores motivados para essa inclusão de géneros, torna-se fundamental definir em que medida e com que critérios, é também necessária uma atitude inclusiva de escolas, na técnica de execução interpretativa deste novo género. Através deste estudo deu-se provimento a essa necessida
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Books on the topic "Spectrograms"

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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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R, Shagidullin R., Pudovik A. N, and Institut organicheskǒi i fizicheskǒi khimii im. A. E. Arbuzova., eds. Atlas of IR spectra of organophosphorus compounds: Interpreted spectrograms. NAUKA Publishers, 1990.

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United States. National Aeronautics and Space Administration., ed. Photographic region elemental abundance analyses of Dr. David S. Leckrone's GTO HST stars II: Final report. National Aeronautics and Space Administration, 1994.

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Adelman, Saul J. Photographic region elemental abundance analyses of Dr. David S. Leckrone's GTO HST stars II: Final report. National Aeronautics and Space Administration, 1994.

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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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Martin, Ann. VOICE - a spectrogram computer display package. Woods Hole Oceanographic Institution, 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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Book chapters on the topic "Spectrograms"

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Morgan, Michael M., MacDonald J. Christie, Luis De Lecea, et al. "Spectrograms." In Encyclopedia of Psychopharmacology. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68706-1_1219.

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Thomas, Robert J., and Haoqi Sun. "EEG Spectrograms." In Atlas of Sleep Medicine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-34625-5_15.

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Boggiatto, Paolo, Giuseppe De Donno, and Alessandro Oliaro. "Two-Window Spectrograms and Their Integrals." In Pseudo-Differential Operators: Complex Analysis and Partial Differential Equations. Birkhäuser Basel, 2009. http://dx.doi.org/10.1007/978-3-0346-0198-6_15.

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Pedroche, David Sánchez, Francisco Fariña Salguero, Daniel Amigo Herrero, Jesús García, and José M. Molina. "UAV Airframe Classification Using Acceleration Spectrograms." In 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023). Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-42536-3_4.

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Eversberg, T., J. Dachs, T. W. Berghöfer, C. Huilai, and U. Lemmer. "Analysis of IUE Spectrograms for Be stars." In Pulsation, Rotation and Mass Loss in Early-Type Stars. Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-1030-3_75.

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Cadore, Joyner, Ascensión Gallardo-Antolín, and Carmen Peláez-Moreno. "Morphological Processing of Spectrograms for Speech Enhancement." In Advances in Nonlinear Speech Processing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25020-0_29.

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Barker, Jon. "Missing-Data Techniques: Recognition with Incomplete Spectrograms." In Techniques for Noise Robustness in Automatic Speech Recognition. John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118392683.ch14.

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Goad, Jean W., and John S. Gallagher. "Long-slit echelle spectrograms of Seyfert galaxy nuclei." In Active Galactic Nuclei. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/3-540-19492-4_167.

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McAllister, Tyler, and Björn Gambäck. "Music Style Transfer Using Constant-Q Transform Spectrograms." In Artificial Intelligence in Music, Sound, Art and Design. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-03789-4_13.

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Eickhoff, Patrick, Matthias Möller, Theresa Pekarek Rosin, Johannes Twiefel, and Stefan Wermter. "Bring the Noise: Introducing Noise Robustness to Pretrained Automatic Speech Recognition." In Artificial Neural Networks and Machine Learning – ICANN 2023. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44195-0_31.

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AbstractIn recent research, in the domain of speech processing, large End-to-End (E2E) systems for Automatic Speech Recognition (ASR) have reported state-of-the-art performance on various benchmarks. These systems intrinsically learn how to handle and remove noise conditions from speech. Previous research has shown, that it is possible to extract the denoising capabilities of these models into a preprocessor network, which can be used as a frontend for downstream ASR models. However, the proposed methods were limited to specific fully convolutional architectures. In this work, we propose a nov
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Conference papers on the topic "Spectrograms"

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Wang, Ling, and Li Shi. "Audio detection based on segmented spectrograms." In 2024 2nd International Conference on Computer, Vision and Intelligent Technology (ICCVIT). IEEE, 2024. https://doi.org/10.1109/iccvit63928.2024.10872568.

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Altaf, Muhammad Mohsin, Saadat Ullah Khan, Muhammad Majid, and Syed Muhammad Anwar. "Personality Trait Recognition using ECG Spectrograms and Deep Learning." In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10782328.

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Mortenson, Michael C., Tracianne B. Neilsen, Mark K. Transtrum, and David P. Knobles. "Learning Seabed Properties and Sensitivities from Ship Noise Spectrograms." In OCEANS 2024 - SINGAPORE. IEEE, 2024. http://dx.doi.org/10.1109/oceans51537.2024.10682285.

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S, Sandeep C., and Martin Margala. "Parkinson’s Disease Prediction Using Spectrograms and Deep Belief Networks." In 2025 IEEE 14th International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2025. https://doi.org/10.1109/csnt64827.2025.10968302.

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Cardona, Eduardo Enrique, Abraham Adolfo Rodríguez Zepeda, and Alberto Max Carrasco Bardales. "Development of a Web-Based Diagnostic Tool Using Acoustic Testing and Computer Vision." In I Conferencia Internacional de Ciencia, Tecnología e Innovación. Trans Tech Publications Ltd, 2024. http://dx.doi.org/10.4028/p-ssw44m.

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Acoustic testing is a technology that covers various machinery failure modes, including bearing and gear failures. This technology is superior to vibration analysis for gear and bearing condition monitoring. This paper aims to offer the maintenance world a critical technological advance by developing a web-based tool that, using pretrained convolutional neural networks and spectrograms, allows the diagnosis of gearboxes from recordings obtained with industrial acoustic testing tools. The resulting model is tested against human specialists to assess its actual world performance. A modified agil
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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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R, Shailesh K., Ciji Pearl Kurian, and Savitha G. Kini. "Study of color space transformation techniques for converting spectrographs to spectrograms." In 2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC). IEEE, 2016. http://dx.doi.org/10.1109/icctac.2016.7567343.

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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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Abdel Khaliq, Lotfy, Sabine Janzen, and Wolfgang Maass. "REAVER: Real-time Earthquake Prediction with Attention-based Sliding-Window Spectrograms." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/988.

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Predicting earthquakes with precision remains an ongoing challenge in earthquake early warning systems (EEWS), that struggle with accuracy and fail to provide timely warnings for impending earthquakes. Recent efforts employing deep learning techniques have shown promise in overcoming these limitations. However, current methods lack the ability to capture subtle frequency changes indicative of seismic activity in real-time, limiting their effectiveness in EEWS. To address this gap, we propose REAVER, a novel approach for real-time prediction of P- and S-waves of earthquakes using attention-base
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Seelamantula, Chandra Sekhar. "Phase-Encoded Speech Spectrograms." In Interspeech 2016. ISCA, 2016. http://dx.doi.org/10.21437/interspeech.2016-1600.

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Reports on the topic "Spectrograms"

1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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