Relevant bibliographies by topics / Spectrogram analysis

Academic literature on the topic 'Spectrogram analysis'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Spectrogram analysis"

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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 (September 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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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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Ferreira, Diogo R., Tiago A. Martins, and Paulo Rodrigues. "Explainable deep learning for the analysis of MHD spectrograms in nuclear fusion." Machine Learning: Science and Technology 3, no. 1 (December 30, 2021): 015015. http://dx.doi.org/10.1088/2632-2153/ac44aa.

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Abstract In the nuclear fusion community, there are many specialized techniques to analyze the data coming from a variety of diagnostics. One of such techniques is the use of spectrograms to analyze the magnetohydrodynamic (MHD) behavior of fusion plasmas. Physicists look at the spectrogram to identify the oscillation modes of the plasma, and to study instabilities that may lead to plasma disruptions. One of the major causes of disruptions occurs when an oscillation mode interacts with the wall, stops rotating, and becomes a locked mode. In this work, we use deep learning to predict the occurrence of locked modes from MHD spectrograms. In particular, we use a convolutional neural network with class activation mapping to pinpoint the exact behavior that the model thinks is responsible for the locked mode. Surprisingly, we find that, in general, the model explanation agrees quite well with the physical interpretation of the behavior observed in the spectrogram.
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Liao, Ying. "Analysis of Rehabilitation Occupational Therapy Techniques Based on Instrumental Music Chinese Tonal Language Spectrogram Analysis." Occupational Therapy International 2022 (October 3, 2022): 1–12. http://dx.doi.org/10.1155/2022/1064441.

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This paper provides an in-depth analysis of timbre-speech spectrograms in instrumental music, designs a model analysis of rehabilitation occupational therapy techniques based on the analysis of timbre-speech spectrograms in instrumental music, and tests the models for comparison. Starting from the mechanism of human articulation, this paper models the process of human expression as a time-varying linear system consisting of excitation, vocal tract, and radiation models. The system’s overall architecture is designed according to the characteristics of Chinese speech and everyday speech rehabilitation theory (HSL theory). The dual judgment of temporal threshold and short-time average energy realized the phonetic length training. Tone and clear tone training were achieved by linear predictive coding technique (LPC) and autocorrelation function. Using the DTW technique, isolated word speech recognition was achieved by extracting Mel-scale Frequency Cepstral Coefficients (MFCC) parameters of speech signals. The system designs corresponding training scenes for each training module according to the extracted speech parameters, combines the multimedia speech spectrogram motion situation with the speech parameters, and finally presents the training content as a speech spectrogram, and evaluates the training results through human-machine interaction to stimulate the interest of rehabilitation therapy and realize the speech rehabilitation training of patients. After analyzing the pre- and post-test data, it was found that the p -values of all three groups were <0.05, which was judged to be significantly different. Also, all subjects changed their behavioral data during the treatment. Therefore, it was concluded that the music therapy technique could improve the patients’ active gaze communication ability, verbal command ability, and active question-answering ability after summarizing the data, i.e., the hypothesis of this experiment is valid. Therefore, it is believed that the technique of timbre-speech spectrogram analysis in instrumental music can achieve the effect of rehabilitation therapy to a certain extent.
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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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Oh, Myeonggeun, and Yong-Hoon Kim. "Statistical Approach to Spectrogram Analysis for Radio-Frequency Interference Detection and Mitigation in an L-Band Microwave Radiometer." Sensors 19, no. 2 (January 14, 2019): 306. http://dx.doi.org/10.3390/s19020306.

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For the elimination of radio-frequency interference (RFI) in a passive microwave radiometer, the threshold level is generally calculated from the mean value and standard deviation. However, a serious problem that can arise is an error in the retrieved brightness temperature from a higher threshold level owing to the presence of RFI. In this paper, we propose a method to detect and mitigate RFI contamination using the threshold level from statistical criteria based on a spectrogram technique. Mean and skewness spectrograms are created from a brightness temperature spectrogram by shifting the 2-D window to discriminate the form of the symmetric distribution as a natural thermal emission signal. From the remaining bins of the mean spectrogram eliminated by RFI-flagged bins in the skewness spectrogram for data captured at 0.1-s intervals, two distribution sides are identically created from the left side of the distribution by changing the standard position of the distribution. Simultaneously, kurtosis calculations from these bins for each symmetric distribution are repeatedly performed to determine the retrieved brightness temperature corresponding to the closest kurtosis value of three. The performance is evaluated using experimental data, and the maximum error and root-mean-square error (RMSE) in the retrieved brightness temperature are served to be less than approximately 3 K and 1.7 K, respectively, from a window with a size of 100 × 100 time–frequency bins according to the RFI levels and cases.
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Pizetta, Igor Henrique Beloti, Alexandre Santos Brandão, and Mário Sarcinelli-Filho. "UAV Thrust Model Identification Using Spectrogram Analysis." Automation 2, no. 3 (August 1, 2021): 141–52. http://dx.doi.org/10.3390/automation2030009.

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This paper deals with a non-contact method to identify the aerodynamic propeller constants of the Parrot AR.Drone quadrotor. The experimental setup consists of a microphone installed in the flight arena to record audio data. In terms of methodology, a spectrogram analysis is adopted to estimate the propeller velocity based on the filtered sound signal. It is known that, in a hovering maneuver, when the UAV mass increases, the propellers rotate faster to produce the necessary thrust increment. In this work, the rotorcraft takes off with its factory settings, first with no hull, corresponding to a mass of 413 g, and after with a small hull, corresponding to a mass of 444 g, and a bigger hull, corresponding to a mass of 462 g. In the sequence, the velocity of the propellers are estimated for each of these three cases using spectrograms of audio recorded by a microphone, corresponding to the sound generated by the four rotors. Finally, the estimated velocity is used to identify the aerodynamic parameters, thus validating the proposal.
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Franzoni, Valentina, Giulio Biondi, and Alfredo Milani. "Emotional sounds of crowds: spectrogram-based analysis using deep learning." Multimedia Tools and Applications 79, no. 47-48 (August 17, 2020): 36063–75. http://dx.doi.org/10.1007/s11042-020-09428-x.

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AbstractCrowds express emotions as a collective individual, which is evident from the sounds that a crowd produces in particular events, e.g., collective booing, laughing or cheering in sports matches, movies, theaters, concerts, political demonstrations, and riots. A critical question concerning the innovative concept of crowd emotions is whether the emotional content of crowd sounds can be characterized by frequency-amplitude features, using analysis techniques similar to those applied on individual voices, where deep learning classification is applied to spectrogram images derived by sound transformations. In this work, we present a technique based on the generation of sound spectrograms from fragments of fixed length, extracted from original audio clips recorded in high-attendance events, where the crowd acts as a collective individual. Transfer learning techniques are used on a convolutional neural network, pre-trained on low-level features using the well-known ImageNet extensive dataset of visual knowledge. The original sound clips are filtered and normalized in amplitude for a correct spectrogram generation, on which we fine-tune the domain-specific features. Experiments held on the finally trained Convolutional Neural Network show promising performances of the proposed model to classify the emotions of the crowd.
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Sun, Hong-ying, Hong-wu Yang, and Zhong-xing Tao. "Realization of Spectrogram Analysis with FPGA." Journal of Electronics & Information Technology 33, no. 5 (May 12, 2011): 1238–42. http://dx.doi.org/10.3724/sp.j.1146.2010.01005.

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ELEMANS, COEN P. H., KIER HEECK, and MEES MULLER. "SPECTROGRAM ANALYSIS OF ANIMAL SOUND PRODUCTION." Bioacoustics 18, no. 2 (January 2008): 183–212. http://dx.doi.org/10.1080/09524622.2008.9753599.

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Dissertations / Theses on the topic "Spectrogram analysis"

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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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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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Yao, Bing. "ANALYSIS OF ELECTRICAL AND MAGNETIC BIO-SIGNALS ASSOCIATED WITH MOTOR PERFORMANCE AND FATIGUE." Case Western Reserve University School of Graduate Studies / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=case1140813534.

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Krejčí, Michal. "Fourierova transformace a spektrogramy v analýze DNA sekvencí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-219249.

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Various methods of DNA sequences modifications for frequency analysis and basic characteristics of DNA are described in the theoretical part of this thesis. Tricolor spectrograms, created by short time Fourier transform help us to recognize some characteristic patterns in DNA sequences. Practical part of this work deals with developed programme which generates spectrograms and analyse them. Last part deals with the analysis of selected sequences of C. elegans genome. Some patterns are related to data of public databases such as NCBI. Various patterns are explained from the biological nature, which relates to chromosome structure and protein coding regions. Another well recognised patterns, tandem repetitions composed of satellites, microsatellites and minisatelites are described by spectrograms as well.
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DAK, HAZIRBABA YILDIZ. "IMAGE-BASED MODELING AND PREDICTION OF NON-STATIONARY GROUND MOTIONS." OpenSIUC, 2015. https://opensiuc.lib.siu.edu/dissertations/1008.

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Nonlinear dynamic analysis is a required step in seismic performance evaluation of many structures. Performing such an analysis requires input ground motions, which are often obtained through simulations, due to the lack of sufficient records representing a given scenario. As seismic ground motions are characterized by time-varying amplitude and frequency content, and the response of nonlinear structures is sensitive to the temporal variations in the seismic energy input, ground motion non-stationarities should be taken into account in simulations. This paper describes a nonparametric approach for modeling and prediction of non-stationary ground motions. Using Relevance Vector Machines, a regression model which takes as input a set of seismic predictors, and produces as output the expected evolutionary power spectral density, conditioned on the predictors. A demonstrative example is presented, where recorded and predicted ground motions are compared in time, frequency, and time-frequency domains. Analysis results indicate reasonable match between the recorded and predicted quantities.
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Kubánková, Anna. "Automatická klasifikace digitálních modulací." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-233424.

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This dissertation thesis deals with a new method for digital modulation recognition. The history and present state of the topic is summarized in the introduction. Present methods together with their characteristic properties are described. The recognition by means of artificial neural is presented in more detail. After setting the objective of the dissertation thesis, the digital modulations that were chosen for recognition are described theoretically. The modulations FSK, MSK, BPSK, QPSK, and QAM-16 are concerned. These modulations are mostly used in modern communication systems. The method designed is based on the analysis of module and phase spectrograms of the modulated signals. Their histograms are used for the examination of the spectrogram properties. They provide information on the count of carrier frequencies in the signal, which is used for the FSK and MSK recognition, and on the count of phase states on which the BPSK, QPSK, and QAM-16 are classified. The spectrograms in that the characteristic attributes of the modulations are visible are obtained with the segment length equal to the symbol length. It was found that it is possible to correctly recognize the modulation with the known symbol length at the signal-to-noise ratio at least 0 dB. That is why it is necessary to detect the symbol length prior to the spectrogram calculation. Four methods were designed for this purpose: autocorrelation function, cepstrum analysis, wavelet transform, and LPC coefficients. These methods were algorithmized and analyzed with signals disturbed by the white Gaussian noise, phase noise and with signals passed through a multipass fading channel. The method of detection by means of cepstrum analysis proved the most suitable and reliable. Finally the new method for digital modulation recognition was verified with signals passed through a channel with properties close to the real one.
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Oliveira, Marlice Fernandes de. "Estudo da separação entre voz patológica e normal por meio da avaliação da energia global do sinal de voz." Universidade Federal de Uberlândia, 2007. https://repositorio.ufu.br/handle/123456789/14661.

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Voice analysis is an important tool in the diagnosis of laryngeal disorders. Among distinct signal processing techniques employed for voice analysis, the spectrogram is commonly used, as it allows for a visualization of the variation of the energy of the signal as a function of the both time and frequency. In this context, this study investigates the use of the global energy of the voice signal, estimated through the spectrogram, as a tool for discrimination between signals obtained from healthy and pathological subjects. This research has also exploited the potential use of the global energy of the voice signal to discriminate distinct laryngeal disorders. In total, 94 subjets were involved in this study, from which 46 were dysphonic and 48 normal. The diagnosis of laryngeal disorders was confirmed by means of a videolaryngoscopic examination. Participants were also subjected to a clinical examination of vocal acoustic through the recording of the sustained vowel. The global energy allowed for the discrimination between normal and disphonic voice. Furthermore, this technique could discriminate the voice signal of patients suffering from left vocal fold paralysis from those suffering from other investigated disorders. The results suggest the global energy of the signal as an auxiliary and alternative tool for the diagnosis between normal and dysphonic voice.
A análise do sinal de voz é uma ferramenta importante no diagnóstico dos distúrbios laríngeos. Dentre as diversas técnicas para o processamento da voz destaca-se o espectrograma por permitir uma visualização da variação da energia do sinal em função do tempo e freqüência. Neste contexto, esta pesquisa investiga a energia global do sinal de voz, estimada a partir do espectrograma, como ferramenta capaz de discriminar esses sinais, obtidos de pacientes com diferentes doenças, daqueles coletados de sujeitos saudáveis. O estudo ainda verifica a possibilidade do uso da energia global na discriminação de distúrbios laríngeos. No total 94 indivíduos participaram desse estudo, sendo 46 disfônicos e 48 eufônicos. Inicialmente os sujeitos foram submetidos a um exame de videolaringoscopia para a determinaçãoção do diagnóstico. Posteriormente eles foram submetidos a um exame clínico de análise acústica vocal por meio da gravação da vogal sustentada. Utilizando a energia global foi possível discriminar as vozes normais das vozes disfônicas. Por meio da energia global foi possível ainda separar as vozes de pacientes portadores de paralisia da prega vocal esquerda de todas as outras doenças laríngeas investigadas. Podemos sugerir a energia global da voz como uma ferramenta auxiliar no diagnóstico diferencial entre vozes normais e disfônicas.
Mestre em Ciências
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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 data based solely on analysis of frequency-domain data. An analytical approach to speech language identification by three comparative techniques is performed. First, a character-based pattern analysis is performed using the Rix and Forster algorithm to replicate their research on language identification. Second, techniques of phoneme recognition and their relative pattern of occurrence in speech samples are measured for performance in ability for language identification using the Rix and Forster approach. Finally, an experiment using statistical analysis of time-ensemble frequency spectrum data is assessed for its ability to establish spectral patterns for language identification, along with performance. This novel approach is applied to spectrogram audio data using pattern analysis techniques for language identification. It applies the Rix and Forster method to the ensemble of spectral frequencies used over the duration of a speech waveform. This novel approach is compared to the applications of the Rix and Forster algorithm to character-based and phoneme symbols for language identification on the basis of statistical accuracy, processing time requirements, and spatial processing resource needs. The audio spectrum analysis also demonstrates the ability to perform speaker identification using the same techniques performed for language identification. The results of this research demonstrate the efficacy of audio frequency-domain pattern analysis applied to speech waveform data. It provides an efficient technique in language identification without reliance upon linguistic approaches using phonemes or word derivations. This work also demonstrates a quick, automated means by which information gatherers, travelers, and diplomatic officials might obtain rapid language identification supporting time-critical determination of appropriate translator resource needs.
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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
0048
新制・課程博士
博士(薬科学)
甲第22651号
薬科博第123号
新制||薬科||13(附属図書館)
京都大学大学院薬学研究科薬科学専攻
(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
学位規則第4条第1項該当
Doctor of Pharmaceutical Sciences
Kyoto University
DFAM
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Movin, Andreas, and Jonathan Jilg. "Kan datorer höra fåglar?" Thesis, KTH, Skolan för teknikvetenskap (SCI), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254800.

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Ljudigenkänning möjliggörs genom spektralanalys, som beräknas av den snabba fouriertransformen (FFT), och har under senare år nått stora genombrott i samband med ökningen av datorprestanda och artificiell intelligens. Tekniken är nu allmänt förekommande, i synnerhet inom bioakustik för identifiering av djurarter, en viktig del av miljöövervakning. Det är fortfarande ett växande vetenskapsområde och särskilt igenkänning av fågelsång som återstår som en svårlöst utmaning. Även de främsta algoritmer i området är långt ifrån felfria. I detta kandidatexamensarbete implementerades och utvärderades enkla algoritmer för att para ihop ljud med en ljuddatabas. En filtreringsmetod utvecklades för att urskilja de karaktäristiska frekvenserna vid fem tidsramar som utgjorde basen för jämförelsen och proceduren för ihopparning. Ljuden som användes var förinspelad fågelsång (koltrast, näktergal, kråka och fiskmås) så väl som egeninspelad mänsklig röst (4 unga svenska män). Våra resultat visar att framgångsgraden normalt är 50–70%, den lägsta var fiskmåsen med 30% för en liten databas och den högsta var koltrasten med 90% för en stor databas. Rösterna var svårare för algoritmen att särskilja, men de hade överlag framgångsgrader mellan 50% och 80%. Dock gav en ökning av databasstorleken generellt inte en ökning av framgångsgraden. Sammanfattningsvis visar detta kandidatexamensarbete konceptbeviset bakom fågelsångigenkänning och illustrerar såväl styrkorna som bristerna av dessa enkla algoritmer som har utvecklats. Algoritmerna gav högre framgångsgrad än slumpen (25%) men det finns ändå utrymme för förbättring eftersom algoritmen vilseleddes av ljud av samma frekvenser. Ytterligare studier behövs för att bedöma den utvecklade algoritmens förmåga att identifiera ännu fler fåglar och röster.
Sound recognition is made possible through spectral analysis, computed by the fast Fourier transform (FFT), and has in recent years made major breakthroughs along with the rise of computational power and artificial intelligence. The technology is now used ubiquitously and in particular in the field of bioacoustics for identification of animal species, an important task for wildlife monitoring. It is still a growing field of science and especially the recognition of bird song which remains a hard-solved challenge. Even state-of-the-art algorithms are far from error-free. In this thesis, simple algorithms to match sounds to a sound database were implemented and assessed. A filtering method was developed to pick out characteristic frequencies at five time frames which were the basis for comparison and the matching procedure. The sounds used were pre-recorded bird songs (blackbird, nightingale, crow and seagull) as well as human voices (4 young Swedish males) that we recorded. Our findings show success rates typically at 50–70%, the lowest being the seagull of 30% for a small database and the highest being the blackbird at 90% for a large database. The voices were more difficult for the algorithms to distinguish, but they still had an overall success rate between 50% and 80%. Furthermore, increasing the database size did not improve success rates in general. In conclusion, this thesis shows the proof of concept and illustrates both the strengths as well as short-comings of the simple algorithms developed. The algorithms gave better success rates than pure chance of 25% but there is room for improvement since the algorithms were easily misled by sounds of the same frequencies. Further research will be needed to assess the devised algorithms' ability to identify even more birds and voices.
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Books on the topic "Spectrogram analysis"

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Astronomical spectrographs and their history. Cambridge: Cambridge University Press, 2009.

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McDermott, A. J. Development of a spectrograph for the analysis of speech. Manchester: UMIST, 1995.

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

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Hymas, Hewitt M. A calibration of the Naval Postgraduate School middle ultraviolet spectrograph and an analysis of the OII 2470 Å emission obtained by the middle ultraviolet spectrograph. Monterey, Calif: Naval Postgraduate School, 1994.

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Marron, Antony C. An analysis of ionospheric dayglow from observations of the Naval Postgraduate School Middle Ultraviolet Spectrograph (MUSTANG). Monterey, Calif: Naval Postgraduate School, 1993.

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a, M. Time Frequency Analysis: Comparisons Between WVD and Spectrogram with Application. Independently Published, 2019.

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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. [Washington, DC: National Aeronautics and Space Administration, 1994.

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Book chapters on the topic "Spectrogram analysis"

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Lorenzo, Javier, and Mario Hernández. "Habituation Based on Spectrogram Analysis." In Advances in Artificial Intelligence — IBERAMIA 2002, 893–902. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36131-6_91.

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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, 543–50. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25751-8_65.

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Chassande-Mottin, Eric, Patrick Flandrin, and François Auger. "On the Statistics of Spectrogram Reassignment Vectors." In Recent Developments in Time-Frequency Analysis, 23–30. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2838-5_3.

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Le Roux, Jonathan, Emmanuel Vincent, Yuu Mizuno, Hirokazu Kameoka, Nobutaka Ono, and Shigeki Sagayama. "Consistent Wiener Filtering: Generalized Time-Frequency Masking Respecting Spectrogram Consistency." In Latent Variable Analysis and Signal Separation, 89–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15995-4_12.

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Drgas, Szymon, and Tuomas Virtanen. "Speaker Verification Using Adaptive Dictionaries in Non-negative Spectrogram Deconvolution." In Latent Variable Analysis and Signal Separation, 462–69. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22482-4_54.

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Duc, Trinh Quang, Nguyen Van Son, Nguyen Hoai Giang, Dao Huy Du, and Ha Ngoc Thu. "An Analysis of Lung Sound from Electronic Stethoscope with Spectrogram." In Advances in Engineering Research and Application, 107–13. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37497-6_12.

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Bodusz, Wojciech, Zuzanna Miodońska, and Paweł Badura. "Approach for spectrogram analysis in detection of selected pronunciation pathologies." In Innovations in Biomedical Engineering, 3–11. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70063-2_1.

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Oswald, Julie N., Christine Erbe, William L. Gannon, Shyam Madhusudhana, and Jeanette A. Thomas. "Detection and Classification Methods for Animal Sounds." In Exploring Animal Behavior Through Sound: Volume 1, 269–317. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97540-1_8.

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AbstractClassification of the acoustic repertoires of animals into sound types is a useful tool for taxonomic studies, behavioral studies, and for documenting the occurrence of animals. Classification of acoustic repertoires enables the identification of species, age, gender, and individual identity, correlations between sound types and behavior, the identification of changes in vocal behavior over time or in response to anthropogenic noise, comparisons between the repertoires of populations living in different geographic regions and environments, and the development of software tools for automated signal processing. Techniques for classification have evolved over time as technical capabilities have expanded. Initially, researchers applied qualitative methods, such as listening and visually discerning sounds in spectrograms. Advances in computer technology and the development of software for the automatic detection and classification of sounds have allowed bioacousticians to quickly find sounds in recordings, thus significantly reducing analysis time and enabling the analysis of larger datasets. In this chapter, we present software algorithms for automated signal detection (based on energy, Teager–Kaiser energy, spectral entropy, matched filtering, and spectrogram cross-correlation) as well as for signal classification (e.g., parametric clustering, principal component analysis, discriminant function analysis, classification trees, artificial neural networks, random forests, Gaussian mixture models, support vector machines, dynamic time-warping, and hidden Markov models). Methods for evaluating the performance of automated tools are presented (i.e., receiver operating characteristics and precision-recall) and challenges with classifying animal sounds are discussed.
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Yuan, Longhao, and Jianting Cao. "Patients’ EEG Data Analysis via Spectrogram Image with a Convolution Neural Network." In Intelligent Decision Technologies 2017, 13–21. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59421-7_2.

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Souli, Sameh, Zied Lachiri, and Alexander Kuznietsov. "Using Three Reassigned Spectrogram Patches and Log-Gabor Filter for Audio Surveillance Application." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 527–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41822-8_66.

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Conference papers on the topic "Spectrogram analysis"

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Zawawi, T. N. S. T., A. R. Abdullah, E. F. Shair, I. Halim, and O. Rawaida. "Electromyography signal analysis using spectrogram." In 2013 IEEE Student Conference on Research and Development (SCOReD). IEEE, 2013. http://dx.doi.org/10.1109/scored.2013.7002599.

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Doura, Tomoki, and Toshihiko Shiraishi. "Sound Source Separation Using Spectrogram Analysis by Neural Networks." In ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-71583.

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The performance of neural networks has been dramatically improved since the method called “deep leaning” was developed around 2006[1][2]. Mainly, neural networks have been used for classification problems such as visual pattern recognition and speech recognition. However, there are not so many studies of sound source separation using neural networks. To apply neural networks to separation problems, separation problems require to be transformed into classification problems. To realize it, we referred to spectrogram analysis by specialists. Specialists can separate each source signal from the spectrogram of mixed signals by focusing on each local area of the spectrogram. In this study, we developed a novel method for sound source separation using spectrogram analysis by neural networks. As a result of the simulation, we successfully separated male and female voices from their mixed sound. The proposed method is superior to conventional methods in separation problems with sound reflection on walls and convolutional mixture which includes the difference of traveling time from a sound source to microphones because the method does not require to identify the mixture process in space.
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Xu, Qiang. "Jet noise analysis by Gabor spectrogram." In Defense and Security Symposium, edited by Harold H. Szu. SPIE, 2006. http://dx.doi.org/10.1117/12.655467.

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Benedetto, John J., and David Colella. "Wavelet analysis of spectrogram seizure chirps." In SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, edited by Andrew F. Laine and Michael A. Unser. SPIE, 1995. http://dx.doi.org/10.1117/12.217606.

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Patzold, Matthias, and Neji Youssef. "Spectrogram analysis of multipath fading channels." In 2015 IEEE 26th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). IEEE, 2015. http://dx.doi.org/10.1109/pimrc.2015.7343665.

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Garcia-Casado, J., J. L. Martinez-de-Juan, G. Prats-Boluda, J. Bertelli, and J. L. Ponce. "Adaptive Spectrogram for Surface EEnG Analysis." In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1616546.

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Dugnol, B., C. Fernández, G. Galiano, and J. Velasco. "Evolution Nonlinear Diffusion‐Convection PDE Models for Spectrogram Enhancement." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS: International Conference on Numerical Analysis and Applied Mathematics 2008. American Institute of Physics, 2008. http://dx.doi.org/10.1063/1.2990882.

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Li, Jiarui, and Ying Hong. "Wheeze Detection Algorithm Based on Spectrogram Analysis." In 2015 8th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2015. http://dx.doi.org/10.1109/iscid.2015.310.

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Latif, R., E. Aassif, M. Laaboubi, and G. Maze. "Dimensional characterization of an elastic tube using the spectrogram and the reassigned spectrogram time-frequency analysis." In 2010 4th International Symposium on Communications, Control and Signal Processing (ISCCSP). IEEE, 2010. http://dx.doi.org/10.1109/isccsp.2010.5463487.

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Chu, Jianjun, Yuejuan Jiang, Quanchen Li, and Dazun Zhao. "Interferogram processing with wavelet analysis and spectrogram reconstruction." In Optics and Optoelectronic Inspection and Control: Techniques, Applications, and Instruments, edited by Shulian Zhang and Wei Gao. SPIE, 2000. http://dx.doi.org/10.1117/12.403916.

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Reports on the topic "Spectrogram analysis"

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Sylvia, J. M., J. W. Haas, K. M. Spencer, M. M. Carrabba, R. D. Rauh, R. W. Forney, and T. M. Johnston. Field Raman Spectrograph for Environmental Analysis. Office of Scientific and Technical Information (OSTI), July 1998. http://dx.doi.org/10.2172/3969.

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