Relevant bibliographies by topics / Spectrogram

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  2. Dissertations / Theses
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  4. Book chapters
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Academic literature on the topic 'Spectrogram'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 June 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 a self-supervised hidden representation of the generator to a conditional discriminator. This leads to better guidance for generator training. Moreover, we also propose adversarial style combination (ASC) for better generalization in the unseen speaking style and transcript, which can learn latent representations of the combined style embedding from multiple mel-spectrograms. Trained with ASC and feature matching, the MSG synthesizes a high-diversity mel-spectrogram by controlling and mixing the individual speaking styles (e.g., duration, pitch, and energy). The result shows that the MSG synthesizes a high-fidelity mel-spectrogram, which has almost the same naturalness MOS score as the ground-truth mel-spectrogram.
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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 students of engineering backgrounds is presented. Findings from the implementation of these teaching methods in undergraduate and graduate engineering courses at University of California, Los Angeles are also unveiled.
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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 that incorporates Bottleneck Residual Blocks to improve training stability and computational efficiency. The experimental results show that the model trained with Mel-spectrogram data achieved the highest classification accuracy at 97.13%. The models trained with spectrogram and MFCC data achieved 95.22% and 93.78% accuracy, respectively. The superior performance of the Mel-spectrogram model is attributed to its ability to emphasize critical acoustic features through Mel-filter banks, which enhances learning performance. These findings demonstrate the effectiveness of spectrogram-based deep learning models in identifying latent patterns within acoustic data and detecting abnormal symptoms. Future research will focus on applying this approach to a wider range of acoustic domains and environments. The results of this study are expected to contribute to the development of disease surveillance systems by integrating acoustic data analysis with artificial intelligence techniques.
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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, collected from 40 speakers, show that this methodology can be effectively used to produce a very high success rate in a closed set of speakers for text-dependent speaker identification. AMS (2000) Subject Classification: 62P99.
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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 spectrogram and extracts the frequencies from the CFEDs. First, the VLF waveform data collected with the CSES electric field detector (EFD) are converted into a time–frequency spectrogram using short-time Fourier Transform (STFT). Next, the CFED automatic recognition algorithm is used to identify horizontal lines on the spectrogram. The third step is to determine the line frequency range based on the proportional relationship between the frequency domain of the satellite’s VLF and the height of the time–frequency spectrogram. Finally, we used the CSES power spectrogram to confirm the presence of CFEDs in the line frequency range and extract their true frequencies. We statistically analyzed 1034 orbit time–frequency spectrograms and power spectrograms from 8 periods (5 days per period) and identified approximately 200 CFEDs. Among them, two CFEDs with strong signals persisted throughout an entire orbit. This study establishes a foundation for detecting anomalies due to artificial sources, particularly in the study of short-term strong earthquake prediction. Additionally, it contributes to research on other aspects of spatial electromagnetic interference and the suppression and cleaning of electromagnetic waves.
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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 satisfactory estimation accuracy in experiments when compared with various other approaches. When using only the Jamendo and FMA datasets, the estimation accuracy experiences an average error of 4.9% and 1.8%, respectively. Moreover, the BSQ approach holds advantages over some of the comparison counterparts. Overall, it presents a promising approach for estimating the accuracy of a deep network model.
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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, along with the increasing of the capillary electrophoresis spectrogram noise level, the related components’ ability of the two kinds of the overlapping capillary electrophoresis spectrogram by neural network model quantitative analysis drop down. Along with the increasing of the capillary electrophoresis spectrogram’s total dissociation degree, the multi-layered perceptron neural network model to the related components’ ability of the overlapping capillary electrophoresis spectum by quantitative analysis raise up.
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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 first channel, a CNN is used to extract the low-frequency information of the Mel spectrogram. The other channel extracts the high-frequency information of the IMel spectrogram. This information is transmitted into an SSAE to reduce the number of dimensions, and obtain the optimized information. Experimental results show that the highest recognition rates achieved on the EMO-DB, SAVEE, and RAVDESS datasets were 94.79%, 88.96%, and 83.18%, respectively. The conclusions are that the recognition rate of the two spectrograms was higher than that of each of the single spectrograms, which proves that the two spectrograms are complementary. The SSAE followed the CNN to get the optimized information, and the recognition rate was further improved, which proves the effectiveness of the CNN-SSAE network.
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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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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 namely Log Spectrogram, Gammatonegram and IIR-CQT Spectrogram for audio samples from the standardized IIIT-H Indic Speech Database. These visual representations depict language specific details and the nature of each language. These spectrograms images were then used as an input to the CNN. Classification accuracy of 98.86% was obtained using the proposed methodology.
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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 investigation into low-level feature detection reveals that none of the evaluated methods perform adequately within the low signal-to-noise ratios of real-life spectrograms and, therefore, two novel feature detectors are proposed. An investigation into the various sources of information available to the detection process shows that the most simple of these, the individual pixel intensity values, used by most existing algorithms, is not sufficient for the problem. To overcome these limitations, a novel low-level feature detector is integrated into a novel active contour track detection algorithm, and this serves to greatly increase detection rates at low signal-to-noise ratios. Furthermore, the algorithm integrates a priori knowledge of the harmonic process, which describes the relative positions of tracks, to augment the available information in difficult conditions. Empirical evaluation of the algorithm demonstrates that it is effective at detecting tracks at signal-to-noise ratios as low as: 0.5 dB with vertical; 3 dB with oblique; and 2 dB with sinusoidal variation of harmonic features. It is also concluded that the proposed potential energy increases the active contour's effectiveness in detecting all the track structures by a factor of eight (as determined by the line location accuracy measure), even at relatively high signal-to-noise ratios, and that incorporating a priori knowledge of the harmonic process increases the detection rate by a factor of two.
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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 power by analyzing each window independently, even though nearby windows have similar spectral properties. The state-space multitaper algorithm combines two approaches for spectral analysis: the state-space approach models the relations between nearby windows, and the multitaper approach balances a bias-variance tradeoff inherent in Fourier analysis of finite interval data. I illustrate an application of the algorithm to real-time anesthesia monitoring, which could prevent traumatic cases of intraoperative awareness. I discuss issues including a real-time implementation and modeling the system's noise parameters. I identify the new problem of phase censorship, by which spectral leakage hides some information necessary to relate signal phases across windows in time.<br>by Michael K. Behr.<br>M. Eng.
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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 listenable reconstructions of surprising fidelity compared to uncompressed versions, even for out-of-domain examples. The results suggest that the learned discrete quantization method achieves about 9x harder spectrogram compression compared to its continuous counterpart, while achieving similar reconstructions, both qualitatively and in terms of quantitative error metrics.<br>Trots de senaste framgångarna för neurala nätverk på en rad olika områden är musikalisk ljudmodellering fortfarande en svår uppgift, med karakteristiska egenskaper som spänner över tiotusentals dimensioner i inputrymnden. Genom att formulera ljuddatakomprimering som en oövervakad inlärningsuppgift undersöker detta projekt användbarheten av vektorkvantiserade neurala nätverkbaserade självkodare på spektrogram – en bildliknande representation av ljud. Med en nyligen beskriven gradientbaserad metod för approximering av vågformer från rekonstruerade (realvärda) spektrogram, producerar den diskreta pipelinen lyssningsbara rekonstruktioner med överraskande ljudåtergivning jämfört med okomprimerade versioner, även för exempel utanför domänen. Resultaten tyder på att den lärda diskreta kvantiseringsmetoden uppnår ungefär nio gånger hårdare spektrogramkompression jämfört med sin kontinuerliga motsvarighet, samtidigt som den skapar liknande rekonstruktioner, både kvalitativt och enligt kvantitativa felmått.
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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 reassignment called differential reassignment is modified and superimposed with nonlinear anisotropic diffusion. This study first examines reassignment and diffusion components individually. Three different versions of differential reassignment are considered, two of which are modifications explicitly derived in this research. The combined algorithm is then applied to reassign experimentally measured spectrograms, leading to a significant increase in clarity and notch detection performance.
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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 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.
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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 concept of allowed deviation is not provided by theory. Comparing our results to the notion of pitch discrimination from psychophysics we further established that the frequency differences are minute. The Attraction Effect was tested for a secondary tone (E) and the effect is quantified for the first time. The concept of the Attraction Effect is not explained in theory in terms of frequencies of tones.
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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 características de uma paisagem, área denominada análise de Paisagens Acústicas. Nesta pesquisa de mestrado é apresentada uma abordagem visual para análise de Paisagens Acústicas. Essa abordagem consiste em duas etapas, sendo que na primeira são aplicadas técnicas visuais (Projeção Multidimensional t-SNE) e numéricas (Coeficiente de Silhueta) para avaliar quais conjuntos de características melhor descrevem uma Paisagem Acústica específica. A segunda etapa utiliza técnicas de visualização para analisar diferenças globais e características específicas de paisagens terrestres e aquáticas. Para isso foram utilizados o Mapa de Calor, as Coordenadas Paralelas e a xHiPP, uma extensão da projeção HiPP. A xHiPP busca melhorar a HiPP para alcançar melhor capacidade de análise e flexibilidade de aplicação. Com a aplicação das etapas apresentadas foi possível encontrar evidências de que o Mel-frequency Cepstrum Coefficients formam um conjunto de atributos eficaz para representação e segregação de Paisagens Acústicas. Também foi possível verificar que as técnicas de visualização empregadas na análise são capazes de destacar atributos semelhantes dos áudios, facilitando a análise, permitindo que o usuário dê enfoque às características relevantes do ambiente, no lugar de analisar áudios individuais para extrair informações.<br>The growth of data volume caused by current technological development has been strongly evoked as a premise for use of techniques that help exploration, analysis, and understanding of data. A subset of these techniques is yielded by the field of Data Visualization, which provides visual manners to identify patterns and trends, as well as the extraction of hidden data features. Such approaches can be applied to problems that aim at data analysis with a strong exploratory component. One such problem is the use of sound as a tool to describe environmental landscapes, named ecological Soundscapes. A visual approach to analysis Soundscapes is presented in this master research. The approach contains two steps, and the first step applies visual (tSNE Multidimensional Projection) and numeric (Silhouette Coefficient) techniques to evaluate attributes groups that better describe a specific Soundscape. The second step employs visual techniques to analysis global differences and specific features of the terrestrial and underwater environment. To achieve these goals, the research used Heatmap, Parallel Coordinates, and xHiPP, an extension of HiPP projection. The xHiPP enhanced HiPP to improve its analytical capabilities and flexibility. The presented steps were able to show evidence of the Mel-frequency Cepstrum Coefficients is an effective attribute collection to represent and segregate Soundscapes. As well, visual techniques employed in the analysis are capable to highlight similar audio features, making exploration easy, allowing users to focus relevant environmental attributes, instead of analyzing individual audios to extraction some information.
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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-probing the method may be rationalized. The so-called acoustic Jb-probing method may be a potential complement to the Jb-probing. In this thesis a continued study of the acoustic Jb-probing method is performed by means of a case study in Gubbängen with the focus on the potential additional information that the spectrogram (a visual representation of the frequency spectra) may contain compared to the Jb-parameters alone. This was done by obtaining vibration signals during Jb-probing using a triaxle geophone installed four meters from the boreholes. Vibration signals were collected from 13 boreholes. The vibration signals were then analyzed in time- and frequency domain which were compared to corresponding Jb-parameters and classified soil types. The results showed that the clay layers held the most promise for discovering additional information in the spectrogram, however this does not exclude potential in other soil types. Additionally, it was shown that the geophone ought to be fastened in the ground in order to attain satisfactory data. Overall, the acoustic Jb-probing method is a favorable way of collecting and analyzing data, which with continued development of the operational and computational process may be an economical alternative to the conventional method.
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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. Processing recorded signals and their evaluation is transferred using Matlab 7.7.0 ( R2008 )
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Books on the topic "Spectrogram"

1

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"

1

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 new architecture is also proposed. In our proposal three coherent optical processors take advantage of the rapid input rate and high time-bandwidth product of one-dimensional acousto-optic transducers. Their use permits real-time processing and greatly increases the flexibility of the systems. A simple digital–optical system capable of performing manipulations on the output of the proposed architectures (such as the separation of the positive and negative portions of the GTFR) is also introduced. Finally, results of computer simulations of systems that are designed to produce the ZAM G1TR are presented and compared with spectrogram performance.
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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 Labwindows' MC-MIO-16 interface board - to the IBM Model 70 PS/2 computer. The replicated computer enhanced spectrograms are free of random fluctuation noises that typifies analog chart recorder outputs. The production of the computer-replicated spectrograms occurs from a software program called the "horter.” I have addressed the problem of normalization of the spectrograms, using a second computer program. The overall outcome is a computer- replicated spectrogram having reduced spectra band distortions. These two improvements have implications for increasing the measurable of the line parameters, halfwidths, pressure broadening coefficients, and the line intensities. Measurements have been made for such gases as N2O, and SO2.
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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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