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Dataset results
952 results for “Noise”
Single-cell functional pharmacogenomics reveal cell decision information within gene expression noise
GEO Series GSE138832. Homo sapiens. 19 samples. Type: Expression profiling by high throughput sequencing.
Removing System Noise from Comparative Genomic Hybridization Data by Self-Self Analysis
GEO Series GSE23682. Homo sapiens. 3852 samples. Type: Genome variation profiling by genome tiling array.
Treatment of noise-induced hearing loss by mesenchymal stromal cells: an RNAseq analysis of protective/repair pathways
GEO Series GSE187029. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Probing the effect of promoters on noise in gene expression using thousands of designed sequences
GEO Series GSE55346. synthetic construct. 2 samples. Type: Other.
Thymic epithelia amplify noise in chromatin accessibility via p53 repression to impose immune tolerance [CUT&Run]
GEO Series GSE290716. Mus musculus. 4 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Digital RNA Sequencing Minimizes Sequence-Dependent Bias and Amplification Noise with Optimized Single Molecule Barcodes
GEO Series GSE34449. Escherichia coli str. K-12 substr. MG1655. 2 samples. Type: Expression profiling by high throughput sequencing.
Heat-Shock Pathway Activation by TRC-051384 Protects Spiral Ganglion Neurons from Noise-Induced Hearing Loss [Spatial Transcriptomics]
GEO Series GSE312646. Mus musculus. 51 samples. Type: Other.
A DNA Repair Pathway Can Regulate Transcriptional Noise to Promote Cell Fate Transitions
GEO Series GSE176044. Mus musculus. 19 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome-wide noise controls differentiation potential of mammalian progenitor cells
GEO Series GSE10772. Mus musculus. 11 samples. Type: Expression profiling by array.
Heat-Shock Pathway Activation by TRC-051384 Protects Spiral Ganglion Neurons from Noise-Induced Hearing Loss [RNA-Seq]
GEO Series GSE312476. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Noise exposures causing hearing loss generate proteotoxic stress and activate the proteostasis network
GEO Series GSE160639. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Thymic epithelia amplify noise in chromatin accessibility via p53 repression to impose immune tolerance [P53-WT-cKO_BulkRNA]
GEO Series GSE301724. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Elevated Dnah11 Expression in Hippocampal CaMKII Neurons Impairs Memory via Disrupted Synaptic Plasticity in a Mouse Model of Noise-induced Hidden Hearing Loss
GEO Series GSE300555. Mus musculus. 7 samples. Type: Expression profiling by high throughput sequencing.
Composite macroH2A/NRF-1 Nucleosomes Suppress Noise and Generate Robustness in Gene Expression
GEO Series GSE53133. Homo sapiens. 10 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Cis-regulatory control of transcriptional timing and noise in response to estrogen [RNA-Seq]
GEO Series GSE245120. Homo sapiens. 16 samples. Type: Expression profiling by high throughput sequencing.
The effect of background noise and its removal on the analysis of single-cell expression data
GEO Series GSE218853. Mus musculus. 5 samples. Type: Expression profiling by high throughput sequencing.
Transcriptional noise, gene activation, and roles of SAGA and Mediator Tail measured using nucleotide recoding single cell RNA-seq
GEO Series GSE247795. Saccharomyces cerevisiae. 8 samples. Type: Expression profiling by high throughput sequencing.
Loss of Kat2a enhances transcriptional noise and depletes acute myeloid leukemia stem-like cells [RNA-Seq]
GEO Series GSE118768. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
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>
Data from: Effects of impulsive noise on marine mammals: investigating range-dependent risk
Concerns exist about the impacts of underwater noise on marine mammals. These include auditory damage, which is a significant risk for marine mammals exposed to impulsive sounds such as explosions, pile-driving, and seismic airguns. Currently, impact assessments use different risk criteria for impulsive and non-impulsive sounds (e.g. ships, drilling). However, as impulsive sounds dissipate through the environment, they potentially lose hazardous features (e.g. sudden onset) and become non-impulsive at some distance from the source. Despite management implications, a lack of data on range-dependent characteristics currently limits their inclusion in impact assessments. We address this using acoustic recordings of seismic airguns and pile-driving to quantify range-dependency in impulsive characteristics using four criteria: (i) rise time <25 ms; (ii) quotient of peak pressure and pulse duration >5,000 Pa.s-1; (iii) duration <1 s; (iv) crest factor >15 dB. We demonstrate that some characteristics changed markedly within ranges of ~10 km, and that the mean probability of exceeding criteria (i) and (ii) was <0.5 at ranges >3.5 km. In contrast, the mean probability of exceeding criteria (iii) remained >0.5 up to ~37.0 km, and the mean probability of exceeding criteria (iii) remained <0.5 throughout the range. These results suggest that a proportion of the recorded signals should be defined as impulsive based on each of the criteria, and that some of the criteria change markedly as a result of propagation. However, the impulsive nature of a sound is likely to be a complex interaction of all these criteria, and many other unrelated parameters such as duty cycle, recovery periods, and sound levels will also strongly affect the risk of hearing damage. We recommend future auditory damage studies and impact assessments explicitly consider the ranges at which sounds may lose some of their potentially hazardous characteristics.
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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.