Rumana Islam, Mohammed Tarique · 2024
A non-invasive pathological voice detection system has been presented in this paper. This work considers two spectral images of voice signals namely spectrogram and Mel-spectrogram to detect dysphonic voices. The spectrogram is a convenient representation of voice signals on a time-frequency scale and has been popularly investigated in pathological voice detection algorithms. However, the spectrogram uses a linear frequency scaling and hence, does not consider the resolution of the human auditory model. The Mel-spectrogram overcomes this limitation by using a quasi-logarithmic frequency spacing. This work uses vowel samples available in the Saarbrucken voice database. The simulation results show that the Melspectrogram outperforms the spectrogram in terms of classification accuracy.
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Elena Papale (719029), Marco Gamba (426514), Monica Perez-Gil (719030) et al. · Figshare · 2015
<p>Parameters considered in the study are pointed out. All the sounds showing both a strong signal to noise ratio and not overlap with other vocalisations were extracted. The presence of a whistle was also identified by analyzing the SPLs in the one third octave bands of the signals: we considered the signals when the SPLs were higher of at least 20 dB. We measured four signal parameters for each whistle: beginning frequency, end frequency, minimum frequency and maximum frequency.</p>
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Sean P. Kinahan (14642407), Julie M. Liss (5924550), Visar Berisha (2218258) · Figshare · 2023
<p>Speech production ‘happy’ output audio comparison. The first subplot is DIVA, the second is TorchDIVA, and the bottom is the difference calculated from the two output signals.</p>
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Sean F. Woodward (9028064), Diana Reiss (209312), Marcelo O. Magnasco (9028067) · Figshare · 2020
<p>Displayed is a standard, 1024-bin, Hamming-window spectrogram of one of the 128 tonals that was played (and here sampled) at 192 kHz; frequency resolution of the plot is 187.5 Hz (smoothing added). Note that the spectrogram was constructed from the unplayed, computational source signal (power level is arbitray). Duration = 0.3 s, Number of Cycles = 2, Center Frequency = 10500 Hertz, Cycle Amplitude = 2000 Hertz, Phase Start = -<i>π</i>/2.</p>
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Katie Campbell (2498896), Yelena Cerezke-Riemer (21169987), John H. Acorn (4859542) · Figshare · 2025
A macrophyte bubble stream sound recorded August 1, 2023, at Gull Lake, Alberta, Canada. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0317424#pone.0317424.g003" target="_blank">Fig 1</a> for axis descriptions.
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Quoc Bao Diep (18452207), Hong Yen Phan (18452210), Thanh-Cong Truong (18452213) · Figshare · 2024
<p>Raw data in waveform and representation in spectrogram, mel-spectrogram, and mfcc form.</p>
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Yucai Wang (513301), Wei Chang (442612), Jingjiao Li (4766943) et al. · Figshare · 2024
<div><p>With the increasing amount of data in railway communication system, the conventional wireless high-frequency communication technology cannot meet the requirements of modern communication and needs to be improved. In order to meet the requirements of high-speed signal processing, a high-speed communication signal processing method based on visible light is developed and studied. This method combines the adaptive equalization algorithm with deep learning and is applied to railway communication signal processing. In this research, the wavelength division multiplexing (WDM) and orthogonal frequency division multiplexing (OFDM) techniques are used, and fuzzy C equalization algorithm is used to softly divide the received signals, reduce signal distortion and interference suppression. The experimental results showed that increasing the step size could reduce the equalization effect, while increasing the modulation parameter will increase the bit error rate. Through deep learning to achieve channel equalization, visible light communication could effectively mitigate multi-path transmission and reflection interference, thereby reducing the bit error rate to the level of 0.0001. Under various signal-to-noise ratios, the system using the channel compensation method achieved the lowest bit error rate. This outcome was achieved by implementing hybrid modulation scheme, including Wavelength division multiplexing (WDM) and direct current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) techniques. It has been proved that this method can effectively reduce the channel distortion when the receiver is moving. This study develops a dependable communication system, which enhances signal recovery, reduces interference, and improves the quality and transmission efficiency of railway communication. The system has practical application value in the field of railway communication signal processing.</p></div>
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Katie Campbell (2498896), Yelena Cerezke-Riemer (21169987), John H. Acorn (4859542) · Figshare · 2025
A macrophyte bubble stream sound recorded July 19, 2021, at Gull Lake, Alberta. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0317424#pone.0317424.g003" target="_blank">Fig 1</a> for axis descriptions.
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Coluccelli, Nicola · Zenodo (CERN European Organization for Nuclear Research) · 2023
A) Standoff CARS spectrogram of B. atrophaeus spores. Pump, Stokes, and probe pulse energies are 12, 12, and 9 μJ, respectively. The spore sample is placed at 1 m from the laser system and the integration time is 1 s. (B,D) Cross-sections (solid lines) of standoff CARS spectrogram of B. atrophaeus spores and ALS fitting profiles (dashed lines) for probe delays of 0.5 and 1.5 ps. (C,E) Corresponding residuals and reference Raman spectrum of CaDPA (magenta line). Residuals represent broadband CARS fingerprints of B. atrophaeus spores.
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Horaud, Radu · Fondation Maison des sciences de l'homme · 2015
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Takashi Tonegawa · Zenodo (CERN European Organization for Nuclear Research) · 2023
This dataset contains the ascii data for spectrogram and waveforms observed by distributed acoustic sensing with the Muroto cable.
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Gajjar, Pranshav · TIB Data Manager · 2024
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Tengku Nor Shuhada Binti Tengku Zawawi, A R Abdullah, E F Shair et al. · 2013
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Monstein, Christian, Csillaghy, André, Benz, Arnold O. · Space Physics Data Facility · 2023
This dataset contains solar dynamic spectrogram PNG plots of the Callisto spectrometer data from the e-Callisto network of stations. Each plot spans 15 minutes. From the website http://e-callisto.org - The CALLISTO spectrometer is a programmable heterodyne receiver built in the framework of IHY2007 and ISWI by former Radio and Plasma Physics Group (PI Christian Monstein) at ETH Zurich, Switzerland. The main applications are observation of solar radio bursts and rfi-monitoring for astronomical science, education and outreach. Many CALLISTO instruments have been deployed. A complete list of stations is available from the website.
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Jie Yang, Chenzhou Ye, Yue Zhou · 2012
The aim of modulation classification (MC) is to identify the modulation type of a communication signal. It plays an important role in many cooperative or noncooperative communication applications. Three spectrogram-based modulation classification methods are proposed. Their recognition scope and performance are investigated or evaluated by theoretical analysis and extensive simulation studies. The method taking moment-like features is robust to frequency offset while the other two, which make use of principal component analysis (PCA) with different transformation inputs, can achieve satisfactory accuracy even at low SNR (as low as 2 dB). Due to the properties of spectrogram, the statistical pattern recognition techniques, and the image preprocessing steps, all of our methods are insensitive to unknown phase and frequency offsets, timing errors, and the arriving sequence of symbols.
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Katie Campbell (2498896), Yelena Cerezke-Riemer (21169987), John H. Acorn (4859542) · Figshare · 2025
A macrophyte bubble stream sound recorded at Gull Lake, Alberta, Canada, June 20, 2021. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0317424#pone.0317424.g003" target="_blank">Fig 1</a> for axis descriptions.
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Alessandro Mirra (13884311), Darren Hight (4114735), Claudia Spadavecchia (4111003) et al. · Figshare · 2024
<p>The signal has been recorded from the right prefrontal electrode. Green vertical line: Start of propofol infusion; red vertical line: End of propofol infusion.</p> <p>(PDF)</p>
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Shannon W. Ricci (4339699), DelWayne R. Bohnenstiehl (587634), David B. Eggleston (587633) et al. · Figshare · 2017
<p>A). Spectrogram of a portion of a recording taken in Harris Creek in May 2015 shows detected toadfish boatwhistles. The black circles indicate the first and second harmonic detection frequencies. B) The detection score plot shows the associated detection score for each boatwhistle, with circles denoting the detection. The detection score represents the highest correlation value, returned from the suite of kernels, at each time step. The threshold for declaring a detection is determined empirically (See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0182757#sec002" target="_blank">Methods</a>: Detector sensitivity analysis).</p>
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Jamar Adeebah · UMP Institutional Repository (Universiti Malaysia Pahang) · 2012
Every chemical has its own element or composition. To determine the presence of this element, spectroscopic technique is used. Spectroscopic technique is a study interaction between matter/chemical with electromagnetic radiation (emr). Each chemical compound will have difference absorbance emr spectrum. In this project, the focus will be on Infrared region. In Infrared region, the functional group presence will be found. Chemical sample that have been use is polyethylene. Polyethylene will be undergoing degradation process with Manganese Laureate. \nYield solution of the process will analyze by FTIR and the image of spectrogram will be recorded. In field of Electrical and Electronics Engineering field, the presence of functional group is determined based on image of spectrogram. As conclusion, hope this project will give impact to chemist to make analysis of chemical.
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Loris Nanni, Andrea Rigo, Alessandra Lumini et al. · Applied Sciences · 2020
In this work, we combine a Siamese neural network and different clustering techniques to generate a dissimilarity space that is then used to train an SVM for automated animal audio classification. The animal audio datasets used are (i) birds and (ii) cat sounds, which are freely available. We exploit different clustering methods to reduce the spectrograms in the dataset to a number of centroids that are used to generate the dissimilarity space through the Siamese network. Once computed, we use the dissimilarity space to generate a vector space representation of each pattern, which is then fed into an support vector machine (SVM) to classify a spectrogram by its dissimilarity vector. Our study shows that the proposed approach based on dissimilarity space performs well on both classification problems without ad-hoc optimization of the clustering methods. Moreover, results show that the fusion of CNN-based approaches applied to the animal audio classification problem works better than the stand-alone CNNs.
Cited by 41Open access