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6 results for “Noise Estimate”
Weekly noise estimate of the residual of the LDC2a data set
<p>Weekly noise estimate of the residual of the LDC2a data set. The recovered Galactic binaries and massive black hole binaries are subtracted for each week.</p>
Code for noise-based seismic velocity changes estimation with the Bezymianny volcano data set. Journal of Volcanology and Geothermal Research.
<p>This file contains all the data and the python scripts used to estimate seismic velocity changes for the Bezymianny volcano (Klyuchevskoy volcano group). It also includes a guideline README.pdf with the description how to reproduce all the results presented in the paper <strong>Berezhnev Y., Belovezhets N., Shapiro N., Koulakov I. (2022), Temporal changes of seismic velocities below Bezymianny volcano prior to its explosive eruption on 20.12.2017, Journal of Volcanology and Geothermal Research</strong></p>
Migration of mechanical perturbations estimated by seismic coda wave interferometry during the 2018 pre-eruptive period at Kīlauea volcano, Hawaii : Noise Cross-correlation Functions, Seismic catalog, and GNSS data
<p>ARCHIVE_NCFs_KILAUEA_2018.zip : Compress folder with (1) the daily noise cross-correlation functions (in MSEED format) of the station pairs used in the paper and (2) the one hour noise cross-correlation functions (in H5 format) of the station pairs used in the figure 9 of the paper.</p> <p>Code_Data_HVO.ipynb : Code to download the seismic data, available on IRIS, used in this paper.</p> <p>GPS_data_AHUP.zip : Compress folder with the daily GPS data of the station AHUP used in the paper [Year, Month, Day, Day_of_the_year, Second_of_the_day, East_comp(mm), North_comp(mm), Vertical_comp(mm), Sig_East_comp, Sig_North_comp, Sig_Vertical_comp].</p> <p>Radial_tilt_UWD.txt : Daily radial tilt measurement of the tiltmeter UWD [Year, Month, Day, Radial_tilt(µrad)].</p> <p>Seismic_stations_Kilauea.txt : Name code and location of the seismic stations used in the paper [Station_code, Longitude, Latitude].</p> <p>Seismicity_Catalog_Kilauea_2018_USGS.txt : Seismic catalog from USGS used in the paper [Date_Time, Latitude, Longitude, Depth, Magnitude].</p>
Data from: Separating biological signal from methodological noise in home range estimates
Open the record for dataset details and reuse information.
Power spectral density estimates of underwater wind and rain noise from the northeast Pacific continental margin
<p>Power spectral density (PSD) estimates of of underwater wind and rain noise used in the papers</p> <ul> <li> <p>Schwock, F., & Abadi, S. (2021a). Characterizing underwater noise during rain at the northeast Pacific continental margin. <em>The Journal of the Acoustical Society of America</em>, <em>149</em>(6), 4579–4595. https://doi.org/10.1121/10.0005440</p> </li> <li> <p>Schwock, F., & Abadi, S. (2021b). Statistical analysis and modeling of underwater wind noise at the northeast pacific continental margin. <em>The Journal of the Acoustical Society of America</em>, <em>150</em>(6), 4166–4177. https://doi.org/10.1121/10.0007463</p> </li> </ul> <p>The PSD estimates were computed using raw data from the Ocean Observatories Initiative (https://ooinet.oceanobservatories.org/). The data and documentation can be downloaded here:</p> <p>https://drive.google.com/drive/u/1/folders/19jvgp_86Ou2zW_GugETP29ZwVK2_GmUb</p>
Speech and Noise Corpora for Pitch Estimation of Human Speech
<p><em>Part of the dissertation <a href="http://localhost:8000/index.html">Pitch of Voiced Speech in the Short-Time Fourier Transform: Algorithms, Ground Truths, and Evaluation Methods</a>.<br> © 2020, Bastian Bechtold. All rights reserved.</em></p> <p> </p> <p>This dataset contains common speech and noise corpora for evaluating fundamental frequency estimation algorithms as convenient <a href="https://jbof.readthedocs.io/en/latest/">JBOF</a> dataframes. Each corpus is available freely on its own, and allows redistribution:</p> <ul> <li><a href="http://www.festvox.org/cmu_arctic/">CMU-ARCTIC</a> (<em>BSD license) [1]</em></li> <li><a href="http://www.cstr.ed.ac.uk/research/projects/fda/">FDA</a> (<em>free to download)</em> [2]</li> <li><a href="https://lost-contact.mit.edu/afs/nada.kth.se/dept/tmh/corpora/KeelePitchDB/">KEELE</a> (<em>free for noncommercial use</em>) [3]</li> <li><a href="http://www.cstr.ed.ac.uk/research/projects/artic/mocha.html">MOCHA-TIMIT</a> (<em>free for noncommercial use</em>) [4]</li> <li><a href="https://www.spsc.tugraz.at/databases-and-tools/ptdb-tug-pitch-tracking-database-from-graz-university-of-technology.html">PTDB-TUG</a> (<em>ODBL license</em>) [5]</li> <li><a href="http://www.speech.cs.cmu.edu/comp.speech/Section1/Data/noisex.html">NOISEX</a> (<em>free to download</em>) [7]</li> <li><a href="https://research.qut.edu.au/saivt/databases/qut-noise-databases-and-protocols/">QUT-NOISE</a> (<em>CC-BY-SA license</em>) [8]</li> </ul> <p>Additionally, this dataset contains <em>PDAs-0.0.1-py3-none-any.whl</em>, a Python ≥ 3.6 module for Linux, containing several well-known fundamental frequency estimation algorithms:</p> <ul> <li>AUTOC [9]</li> <li>AMDF [10]</li> <li><a href="http://www2.ece.rochester.edu/projects/wcng/code.html">BANA</a> [11]</li> <li>CEP [12]</li> <li><a href="https://github.com/marl/crepe">CREPE</a> [13]</li> <li><a href="http://www.kki.yamanashi.ac.jp/~mmorise/world/english/">DIO</a> [14]</li> <li><a href="http://web.cse.ohio-state.edu/pnl/software.html">DNN</a> [15]</li> <li><a href="https://github.com/LvHang/pitch">KALDI</a> [16]</li> <li>MAPS</li> <li><a href="http://www.seas.ucla.edu/spapl/shareware.html">MBSC</a> [17]</li> <li><a href="https://github.com/jkjaer/fastF0Nls">NLS</a> [18]</li> <li><a href="http://www.ee.ic.ac.uk/hp/staff/dmb/voicebox/voicebox.html">PEFAC</a> [19]</li> <li><a href="https://github.com/praat/praat">PRAAT</a> [20]</li> <li><a href="http://www.speech.kth.se/wavesurfer/links.html">RAPT</a> [21]</li> <li><a href="http://labrosa.ee.columbia.edu/projects/SAcC/">SACC</a> [22]</li> <li><a href="http://www.seas.ucla.edu/spapl/weichu/safe/">SAFE</a> [23]</li> <li><a href="https://mathworks.com/matlabcentral/fileexchange/1230">SHR</a> [24]</li> <li>SIFT [25]</li> <li><a href="https://github.com/covarep/covarep">SRH</a> [26]</li> <li><a href="https://github.com/HidekiKawahara/legacy_straight">STRAIGHT</a> [27]</li> <li><a href="http://www.cise.ufl.edu/~acamacho/english/curriculum.html">SWIPE</a> [28]</li> <li><a href="http://www.ws.binghamton.edu/zahorian/yaapt.htm">YAAPT</a> [29]</li> <li><a href="http://audition.ens.fr/adc/">YIN</a> [30]</li> </ul> <p>The algorithms are included in their native programming language (Matlab for BANA, DNN, MBSC, NLS, NLS2, PEFAC, RAPT, RNN, SACC, SHR, SRH, STRAIGHT, SWIPE, YAAPT, and YIN; C for KALDI, PRAAT, and SAFE; Python for AMDF, AUTOC, CEP, CREPE, MAPS, and SIFT), and adapted to a common Python interface. AMDF, AUTOC, CEP, and SIFT are our partial re-implementations as no original source code could be found.</p> <p>All algorithms have been released as open source software, and are covered by their respective licenses.</p> <p>All of these files are published as part of my dissertation, "<a href="https://bastibe.github.io/Dissertation-Website/">Pitch of Voiced Speech in the Short-Time Fourier Transform: Algorithms, Ground Truths, and Evaluation Methods</a>", and in support of the <a href="https://github.com/bastibe/Replication-Dataset-Scripts">Replication Dataset for Fundamental Frequency Estimation</a>.</p> <p>References:</p> <ol> <li>John Kominek and Alan W Black. CMU ARCTIC database for speech synthesis, 2003.</li> <li>Paul C Bagshaw, Steven Hiller, and Mervyn A Jack. Enhanced Pitch Tracking and the Processing of F0 Contours for Computer Aided Intonation Teaching. In EUROSPEECH, 1993.</li> <li>F Plante, Georg F Meyer, and William A Ainsworth. A Pitch Extraction Reference Database. In Fourth European Conference on Speech Communication and Technology, pages 837–840, Madrid, Spain, 1995.</li> <li>Alan Wrench. MOCHA MultiCHannel Articulatory database: English, November 1999.</li> <li>Gregor Pirker, Michael Wohlmayr, Stefan Petrik, and Franz Pernkopf. A Pitch Tracking Corpus with Evaluation on Multipitch Tracking Scenario. page 4, 2011.</li> <li>John S. Garofolo, Lori F. Lamel, William M. Fisher, Jonathan G. Fiscus, David S. Pallett, Nancy L. Dahlgren, and Victor Zue. TIMIT Acoustic-Phonetic Continuous Speech Corpus, 1993.</li> <li>Andrew Varga and Herman J.M. Steeneken. Assessment for automatic speech recognition: II. NOISEX-92: A database and an experiment to study the effect of additive noise on speech recog- nition systems. Speech Communication, 12(3):247–251, July 1993.</li> <li>David B. Dean, Sridha Sridharan, Robert J. Vogt, and Michael W. Mason. The QUT-NOISE-TIMIT corpus for the evaluation of voice activity detection algorithms. Proceedings of Interspeech 2010, 2010.</li> <li>Man Mohan Sondhi. New methods of pitch extraction. Audio and Electroacoustics, IEEE Transactions on, 16(2):262—266, 1968.</li> <li>Myron J. Ross, Harry L. Shaffer, Asaf Cohen, Richard Freudberg, and Harold J. Manley. Average magnitude difference function pitch extractor. Acoustics, Speech and Signal Processing, IEEE Transactions on, 22(5):353—362, 1974.</li> <li>Na Yang, He Ba, Weiyang Cai, Ilker Demirkol, and Wendi Heinzelman. BaNa: A Noise Resilient Fundamental Frequency Detection Algorithm for Speech and Music. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(12):1833–1848, December 2014.</li> <li>Michael Noll. Cepstrum Pitch Determination. The Journal of the Acoustical Society of America, 41(2):293–309, 1967.</li> <li>Jong Wook Kim, Justin Salamon, Peter Li, and Juan Pablo Bello. CREPE: A Convolutional Representation for Pitch Estimation. arXiv:1802.06182 [cs, eess, stat], February 2018. arXiv: 1802.06182.</li> <li>Masanori Morise, Fumiya Yokomori, and Kenji Ozawa. WORLD: A Vocoder-Based High-Quality Speech Synthesis System for Real-Time Applications. IEICE Transactions on Information and Systems, E99.D(7):1877–1884, 2016.</li> <li>Kun Han and DeLiang Wang. Neural Network Based Pitch Tracking in Very Noisy Speech. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(12):2158–2168, Decem- ber 2014.</li> <li>Pegah Ghahremani, Bagher BabaAli, Daniel Povey, Korbinian Riedhammer, Jan Trmal, and Sanjeev Khudanpur. A pitch extraction algorithm tuned for automatic speech recognition. In Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on, pages 2494–2498. IEEE, 2014.</li> <li>Lee Ngee Tan and Abeer Alwan. Multi-band summary correlogram-based pitch detection for noisy speech. Speech Communication, 55(7-8):841–856, September 2013.</li> <li>Jesper Kjær Nielsen, Tobias Lindstrøm Jensen, Jesper Rindom Jensen, Mads Græsbøll Christensen, and Søren Holdt Jensen. Fast fundamental frequency estimation: Making a statistically efficient estimator computationally efficient. Signal Processing, 135:188–197, June 2017.</li> <li>Sira Gonzalez and Mike Brookes. PEFAC - A Pitch Estimation Algorithm Robust to High Levels of Noise. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(2):518—530, February 2014.</li> <li>Paul Boersma. Accurate short-term analysis of the fundamental frequency and the harmonics-to-noise ratio of a sampled sound. In Proceedings of the institute of phonetic sciences, volume 17, page 97—110. Amsterdam, 1993.</li> <li>David Talkin. A robust algorithm for pitch tracking (RAPT). Speech coding and synthesis, 495:518, 1995.</li> <li>Byung Suk Lee and Daniel PW Ellis. Noise robust pitch tracking by subband autocorrelation classification. In Interspeech, pages 707–710, 2012.</li> <li>Wei Chu and Abeer Alwan. SAFE: a statistical algorithm for F0 estimation for both clean and noisy speech. In INTERSPEECH, pages 2590–2593, 2010.</li> <li>Xuejing Sun. Pitch determination and voice quality analysis using subharmonic-to-harmonic ratio. In Acoustics, Speech, and Signal Processing (ICASSP), 2002 IEEE International Conference on, volume 1, page I—333. IEEE, 2002.</li> <li>Markel. The SIFT algorithm for fundamental frequency estimation. IEEE Transactions on Audio and Electroacoustics, 20(5):367—377, December 1972.</li> <li>Thomas Drugman and Abeer Alwan. Joint Robust Voicing Detection and Pitch Estimation Based on Residual Harmonics. In Interspeech, page 1973—1976, 2011.</li> <li>Hideki Kawahara, Masanori Morise, Toru Takahashi, Ryuichi Nisimura, Toshio Irino, and Hideki Banno. TANDEM-STRAIGHT: A temporally stable power spectral representation for periodic signals and applications to interference-free spectrum, F0, and aperiodicity estimation. In Acous- tics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on, pages 3933–3936. IEEE, 2008.</li> <li>Arturo Camacho. SWIPE: A sawtooth waveform inspired pitch estimator for speech and music. PhD thesis, University of Florida, 2007.</li> <li>Kavita Kasi and Stephen A. Zahorian. Yet Another Algorithm for Pitch Tracking. In IEEE International Conference on Acoustics Speech and Signal Processing, pages I–361–I–364, Orlando, FL, USA, May 2002. IEEE.</li> <li>Alain de Cheveigné and Hideki Kawahara. YIN, a fundamental frequency estimator for speech and music. The Journal of the Acoustical Society of America, 111(4):1917, 2002.</li> </ol>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.