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About 10,000 papers for “Spectrogram”
Bowman, Daniel · Zenodo (CERN European Organization for Nuclear Research) · 2025
A minimal working example of circular spectrogram generation using scripts in the R programming language. Example data from the InSight SEIS instrument is used as input for the code.
Cited by 0Open access
Md. Nurul Ahad Tawhid (11027555), Siuly Siuly (11027558), Hua Wang (13530) et al. · Figshare · 2021
<p><b>(a)</b> Images from ASD group, <b>(b)</b> Images from non ASD subjects.</p>
Cited by 0Open access
Simon W. Townsend (282664), Tobias Deschner (324922), Klaus Zuberbühler (42992) · Figshare · 2015
<p>Time-frequency spectrogram of a female copulation call from Budongo Forest during maximum tumescence. Filter bandwidth: 159 Hz, Frequency resolution: 86.1 Hz. Depicted is (A) the total copulation calling bout of approximately 6.5 s and (B) a single copulation call of approximately 0.6 s by the female JL. (C) The lowest visible band is the fundamental frequency from which acoustic measurements were taken with three visible harmonic bands. Copulation calls have a frequency range of 700–1000 Hz.</p>
Cited by 0Open access
G. Jones, B. Boashash · 2005
Cited by 2
Sean A. Fulop · Signals and communication technology · 2011
Cited by 6
Virginia Sciacca (2207953), Francesco Caruso (201563), Laura Beranzoli (828153) et al. · Figshare · 2016
<p>A typical spectrogram (8192 points FFT, 4096 points Hanning window, 97% overlap) of a 10-min recording, computed and analyzed by expert operators. A sequence of 20 Hz calls, here included for clarity in a grey box, has been visually identified in this file.</p>
Cited by 0Open access
Samuel Brudner (15383486), John Pearson (629146), Richard Mooney (423587) · Figshare · 2023
<p>Averages (top rows) of 50 random spectrograms and reconstruction (bottom rows) of corresponding renditions’ mean latent location. Columns (left to right) are generated by sampling renditions produced on 63dph, 73dph, 83dph, and 93dph.</p> <p>(TIF)</p>
Cited by 0Open access
David M. Schruth (11851815), Christopher N. Templeton (4813089), Darryl J. Holman (11851818) · Figshare · 2021
<p>This protocol is also mirrored at <a href="http://doi.org/10.17504/protocols.io.bp5emq3e" target="_blank">http://doi.org/10.17504/protocols.io.bp5emq3e</a>.</p> <p>(DOC)</p>
Cited by 0Open access
Quoc Bao Diep (18452207), Hong Yen Phan (18452210), Thanh-Cong Truong (18452213) · Figshare · 2024
Cited by 0Open access
Gao, Ziqi · DR-NTU (Nanyang Technological University) · 2023
Automatic deception detection is an important research direction, and traditional detection methods based on physiological signals are difficult to be implemented in real life. Video-based detection methods are a better alternative. However, each culture has its own way of expressing deception, and among all the de- ception detection studies, there are more studies for Western cultures. In this project, an Asian ethnic deception detection dataset will be collected. The au- dio spectrograms generated from the videos will be applied to different trans- former models (ViT, DeiT, VPT) for deception detection studies. We will per- form experiments on three public datasets (BoL, Mu3d, RLT) and self-collected datasets. The results of the different models will be compared, showing that DeiT is the most suitable model for automatic deception detection among the three for spectrograms. The results of the self-collected dataset were also com- pared to the public dataset, showing that the self-collected data showed an im- provement of 1.74% and 5.28% for the BoL and Mu3d public datasets, respec- tively.
Cited by 0
William S. D. Wilcock (3749737), Rose S. Hilmo (4537330) · Figshare · 2021
<p>(a) Spectrogram of 30 minutes of data with arbitrary scaling from station J53A starting at 03:30:30 UT on December 11, 2011, showing a sequence of strong Northeast Pacific blue whale calls. An earthquake is also apparent at ~1200 s as a vertical band of energy. (b) As for (a) but normalized by subtracting the median at each frequency and shown with a color scale for values that exceed the 90<sup>th</sup> percentile value. (c) Four-minute segment of the spectrogram from (a) with A and B calls labeled. (d) Cross-correlation kernel that is tuned to the slope of the first half of the B call. (e) Recognition score (RS) for (c) with the minimum threshold of 2.2 shown as a dashed line. Three B calls are strong detections and both the A call and a faint feature early in the spectrogram register as weak detections.</p>
Cited by 0Open access
Cadenhead, Lex, Karayorgi, Anna, Richardson, Jasey et al. · Creative Matter (Skidmore College) · 2019
https://creativematter.skidmore.edu/altered_books_2019/1015/thumbnail.jpg
Cited by 0
Paul Best (5506319), Sébastien Paris (11826884), Hervé Glotin (6400700) et al. · Figshare · 2023
<div><p>The study of non-human animals’ communication systems generally relies on the transcription of vocal sequences using a finite set of discrete units. This set is referred to as a vocal repertoire, which is specific to a species or a sub-group of a species. When conducted by human experts, the formal description of vocal repertoires can be laborious and/or biased. This motivates computerised assistance for this procedure, for which machine learning algorithms represent a good opportunity. Unsupervised clustering algorithms are suited for grouping close points together, provided a relevant representation. This paper therefore studies a new method for encoding vocalisations, allowing for automatic clustering to alleviate vocal repertoire characterisation. Borrowing from deep representation learning, we use a convolutional auto-encoder network to learn an abstract representation of vocalisations. We report on the quality of the learnt representation, as well as of state of the art methods, by quantifying their agreement with expert labelled vocalisation types from 8 datasets of other studies across 6 species (birds and marine mammals). With this benchmark, we demonstrate that using auto-encoders improves the relevance of vocalisation representation which serves repertoire characterisation using a very limited number of settings. We also publish a Python package for the bioacoustic community to train their own vocalisation auto-encoders or use a pretrained encoder to browse vocal repertoires and ease unit wise annotation.</p></div>
Cited by 0Open access
Brad H. Story · NCVS Insights · 2024
This video provides a brief explanation of how spectrograms are constructed as a collection of individual spectra from consecutive points in time across a speech waveform. The difference between “narrow-band” and “wide-band” spectrograms is also explained based on choice analysis window duration. Three-dimensional animations of waveforms, spectra, and spectrograms are used throughout the video to demonstrate spectrographic representations of sound.
Cited by 0
A. Ivanenko (9010331), P. Watkins (9010334), M. A. J. van Gerven (9010337) et al. · Figshare · 2020
Cited by 0Open access
Marcus C. Ng, M. Brandon Westover · Journal of Clinical Neurophysiology · 2021
Cited by 0Open access
Peter Simard (2921550), Kara R. Wall (2921553), David A. Mann (438115) et al. · Figshare · 2016
<p>2048 point resolution spectrogram of typical boat noise produced by an outboard engine driven boat at high speed. Arrows show several harmonics, which here extend upward to about 9000 Hz.</p>
Cited by 0Open access
Danny Parker (504605), Joseph Picone (504606), Amir Harati (504607) et al. · Figshare · 2015
Cited by 0Open access
Flandrin, Patrick, Chassande-Mottin, E., Auger, François · HAL (Le Centre pour la Communication Scientifique Directe) · 2012
International audience
Cited by 0Open access
Gerald T. Beauregard, Mithila Harish, Lonce Wyse · 2015
We present a computationally efficient real-time algorithm for constructing time-domain audio signals from spectrograms. The Single-Pass Spectrogram Inversion (SPSI) algorithm is similar to the synthesis step in phaselocked vocoders, but with phase rates at spectral peaks determined solely from the magnitude spectra using quadratic interpolation. The algorithm provides good quality results in a single fully-deterministic pass, and can also provide excellent initial phase estimates for improved results with iterative spectrogram inversion techniques such as the Griffin-Lim algorithm.
Cited by 38