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Linked collectors and determiners for: CSIC-Real Jardín Botánico-Colección de Plantas Vasculares (MA).
Natural history specimen data linked to collectors and determiners held within, "CSIC-Real Jardín Botánico-Colección de Plantas Vasculares (MA)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/834c9918-f762-11e1-a439-00145eb45e9a">https://bionomia.net/dataset/834c9918-f762-11e1-a439-00145eb45e9a</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/834c9918-f762-11e1-a439-00145eb45e9a">https://gbif.org/dataset/834c9918-f762-11e1-a439-00145eb45e9a</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: CSIC-Real Jardín Botánico-Colección de Líquenes (MA-Lichen).
Natural history specimen data linked to collectors and determiners held within, "CSIC-Real Jardín Botánico-Colección de Líquenes (MA-Lichen)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/83492a58-f762-11e1-a439-00145eb45e9a">https://bionomia.net/dataset/83492a58-f762-11e1-a439-00145eb45e9a</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/83492a58-f762-11e1-a439-00145eb45e9a">https://gbif.org/dataset/83492a58-f762-11e1-a439-00145eb45e9a</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: REAL herbarium - Universidade Federal da Fronteira Sul - Herbário Virtual REFLORA.
Natural history specimen data linked to collectors and determiners held within, "REAL herbarium - Universidade Federal da Fronteira Sul - Herbário Virtual REFLORA". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/7b4d8842-03d4-40e6-b9b1-02274ed48548">https://bionomia.net/dataset/7b4d8842-03d4-40e6-b9b1-02274ed48548</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/7b4d8842-03d4-40e6-b9b1-02274ed48548">https://gbif.org/dataset/7b4d8842-03d4-40e6-b9b1-02274ed48548</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Modelación de la distribución real y potencial de especies de dos géneros de Mimosoideae: Mimosa y Prosopis (Leguminosae) en México (Mimosa).
Natural history specimen data linked to collectors and determiners held within, "Modelación de la distribución real y potencial de especies de dos géneros de Mimosoideae: Mimosa y Prosopis (Leguminosae) en México (Mimosa)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/7162b803-bde2-4196-9383-263ba6dc9ad3">https://bionomia.net/dataset/7162b803-bde2-4196-9383-263ba6dc9ad3</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/7162b803-bde2-4196-9383-263ba6dc9ad3">https://gbif.org/dataset/7162b803-bde2-4196-9383-263ba6dc9ad3</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Modelación de la distribución real y potencial de especies de dos géneros de Mimosoideae: Mimosa y Prosopis (Leguminosae) en México (Prosopis).
Natural history specimen data linked to collectors and determiners held within, "Modelación de la distribución real y potencial de especies de dos géneros de Mimosoideae: Mimosa y Prosopis (Leguminosae) en México (Prosopis)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/2d21ceb2-e459-4025-a62f-6b65c494de47">https://bionomia.net/dataset/2d21ceb2-e459-4025-a62f-6b65c494de47</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/2d21ceb2-e459-4025-a62f-6b65c494de47">https://gbif.org/dataset/2d21ceb2-e459-4025-a62f-6b65c494de47</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: CSIC-Real Jardín Botánico-Colección de Hepáticas (MA-Hepat).
Natural history specimen data linked to collectors and determiners held within, "CSIC-Real Jardín Botánico-Colección de Hepáticas (MA-Hepat)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/8347f6ba-f762-11e1-a439-00145eb45e9a">https://bionomia.net/dataset/8347f6ba-f762-11e1-a439-00145eb45e9a</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/8347f6ba-f762-11e1-a439-00145eb45e9a">https://gbif.org/dataset/8347f6ba-f762-11e1-a439-00145eb45e9a</a>. Formatted as a Frictionless Data package.
Code and data for molecular dating benchmark based on real and simulated Primates gene trees
<p>Benchmark of molecular clock dating applied to single gene trees separately, whose results are described in “Factors influencing the accuracy and precision in dating single gene trees” by Guillaume Louvel and Hugues Roest Crollius.</p> <ol> <li>Real Primates gene trees are analyzed to identify what characteristics of a gene tree are related to the precision of dating; </li> <li>alignments are also simulated on the tree of Primates to measure the accuracy of dating under controlled parameters such as the degree of rate variation and the length of the alignment.</li> </ol> <p><strong>Content</strong></p> <p><code>Louvel_Accuracy-dating_results_2024.tar.gz</code>:<br> - <code>notebook/</code>: statistical analyses in Python;<br> - <code>outputs/</code>: html reports with figures/tables resulting from the analysis;<br> - <code>lib/</code>: required libraries.<br> - <code>data/</code>: intermediate data needed for the final analysis (dates, gene tree features);</p> <p><code>Louvel_Accuracy-dating_dating-source_2024.tar.gz</code>:<br> - <code>dating-source/</code>: input data and config files necessary to reproduce <code>data</code>;</p> <p><code>Louvel_Accuracy-dating_raw-data-preparation_2024.tar.gz</code>:<br> - <code>raw-data-preparation/</code>: raw data and steps to produce <code>dating-source</code>.<br><br><strong>Requirements</strong><br><br>This code requires the libraries developed in the lab for this project,<br>available at <a href="https://github.com/DyogenIBENS/">github.com/DyogenIBENS/</a>, but also included here in <code>lib/</code>.<br><br>- <a href="https://github.com/DyogenIBENS/Phylorgs">Phylorgs</a><br>- <a href="https://github.com/DyogenIBENS/LibsDyogen_py3">LibsDyogen_py3</a><br>- <a href="https://github.com/DyogenIBENS/ToolsDyogen_py3">ToolsDyogen_py3</a>.</p>
A study on real graphs of fake news spreading on Twitter
<p><strong>*** Fake News on Twitter</strong> <strong>***</strong></p> <p>These 5 datasets are the results of an empirical study on the spreading process of newly fake news on Twitter. Particularly, we have focused on those fake news which have given rise to a truth spreading simultaneously against them. The story of each fake news is as follow:</p> <p>1- FN1: A Muslim waitress refused to seat a church group at a restaurant, claiming "religious freedom" allowed her to do so.</p> <p>2- FN2: Actor Denzel Washington said electing President Trump saved the U.S. from becoming an "Orwellian police state."</p> <p>3- FN3: Joy Behar of "The View" sent a crass tweet about a fatal fire in Trump Tower.</p> <p>4- FN4: The animated children's program 'VeggieTales' introduced a cannabis character in August 2018.</p> <p>5- FN5: In September 2018, the University of Alabama football program ended its uniform contract with Nike, in response to Nike's endorsement deal with Colin Kaepernick.</p> <p>The data collection has been done in two stages that each provided a new dataset: 1- attaining Dataset of Diffusion (DD) that includes information of fake news/truth tweets and retweets 2- Query of neighbors for spreaders of tweets that provides us with Dataset of Graph (DG). </p> <p><strong>DD </strong></p> <p>DD for each fake news story is an excel file, named FNx_DD where x is the number of fake news, and has the following structure:</p> <p>The structure of excel files for each dataset is as follow:</p> <ul> <li>Each row belongs to one captured tweet/retweet related to the rumor, and each column of the dataset presents a specific information about the tweet/retweet. These columns from left to right present the following information about the tweet/retweet: </li> <li>User ID (user who has posted the current tweet/retweet)</li> <li>The number of published tweet/retweet by the user at the time of posting the current tweet/retweet</li> <li>Language of the tweet/retweet</li> <li>Number of followers </li> <li>Number of followings (friends)</li> <li>Date and time of posting the current tweet/retweet</li> <li>Number of like (favorite) the current tweet had been acquired before crawling it</li> <li>Number of times the current tweet had been retweeted before crawling it</li> <li>Is there any other tweet inside of the current tweet/retweet (for example this happens when the current tweet is a quote or reply or retweet)</li> <li>The source (OS) of device by which the current tweet/retweet was posted</li> <li>Tweet/Retweet ID</li> <li>Retweet ID (if the post is a retweet then this feature gives the ID of the tweet that is retweeted by the current post)</li> <li>Quote ID (if the post is a quote then this feature gives the ID of the tweet that is quoted by the current post)</li> <li>Reply ID (if the post is a reply then this feature gives the ID of the tweet that is replied by the current post)</li> <li>Frequency of tweet occurrences which means the number of times the current tweet is repeated in the dataset (for example the number of times that a tweet exists in the dataset in the form of retweet posted by others)</li> <li>State of the tweet which can be one of the following forms (achieved by an agreement between the annotators):</li> </ul> <ul> <li>r : The tweet/retweet is a fake news post</li> <li>a : The tweet/retweet is a truth post</li> <li>q : The tweet/retweet is a question about the fake news, however neither confirm nor deny it</li> <li>n : The tweet/retweet is not related to the fake news (even though it contains the queries related to the rumor, but does not refer to the given fake news)</li> </ul> <p> </p> <p><strong>DG</strong></p> <p>DG for each fake news contains two files:</p> <ul> <li>A file in graph format (.graph) which includes the information of graph such as who is linked to whom. (This file named FNx_DG.graph, where x is the number of fake news)</li> <li>A file in Jsonl format (.jsonl) which includes the real user IDs of nodes in the graph file. (This file named FNx_Labels.jsonl, where x is the number of fake news)</li> </ul> <p>Because in the graph file, the label of each node is the number of its entrance in the graph. For example if node with user ID 12345637 be the first node which has been entered into the graph file then its label in the graph is 0 and its real ID (12345637) would be at the row number 1 (because the row number 0 belongs to column labels) in the jsonl file and so on other node IDs would be at the next rows of the file (each row corresponds to 1 user id). Therefore, if we want to know for example what the user id of node 200 (labeled 200 in the graph) is, then in jsonl file we should look at row number 202.</p> <p> </p> <p>The user IDs of spreaders in DG (those who have had a post in DD) would be available in DD to get extra information about them and their tweet/retweet. The other user IDs in DG are the neighbors of these spreaders and might not exist in DD.</p>
RELLISUR: A Real Low-Light Image Super-Resolution Dataset
<p>The RELLISUR dataset contains real low-light low-resolution images paired with normal-light high-resolution reference image counterparts. This dataset aims to fill the gap between low-light image enhancement and low-resolution image enhancement (Super-Resolution (SR)) which is currently only being addressed separately in the literature, even though the visibility of real-world images is often limited by both low-light and low-resolution. The dataset contains 12750 paired images of different resolutions and degrees of low-light illumination, to facilitate learning of deep-learning based models that can perform a direct mapping from degraded images with low visibility to high-quality detail rich images of high resolution. The associated paper can be found here: <a title="https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/7ef605fc8dba5425d6965fbd4c8fbe1f-Paper-round2.pdf" href="https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/7ef605fc8dba5425d6965fbd4c8fbe1f-Paper-round2.pdf" target="_blank" rel="noopener">https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/7ef605fc8dba5425d6965fbd4c8fbe1f-Paper-round2.pdf</a></p>
Real Light Pollution Panorama, by Tomáš Slovinský, Slovakia
<p>Second place in the 2021 IAU OAE Astrophotography Contest, category Light pollution.</p> <p>This composite image taken in Slovakia in 2020 illustrates the effect due to artificial illumination of light polluted areas. The higher the level of light pollution, the less we can observe in the sky, notice how the number of stars visible even to a sensitive digital camera decreases from right to left. Light pollution not only affects the visibility of objects in the night sky, but also significantly impacts ecosystems, negatively affecting many animals, such as migratory night birds, which may encounter difficulties to find the direction to where they should migrate to, or the sea turtles, which may be confused by the lights from coastal cities located near the beaches where they are supposed to spawn. Light pollution can also negatively impact some human health. Therefore, it is important to preserve the dark and quiet night sky for the benefit of the entire planet and all the diverse life it supports.</p> <p>Credit: Tomáš Slovinský/IAU OAE</p>
FIG. 6 in Hypsodont Myomiminae (Gliridae, Rodentia) from five new localities in the Lower Miocene Tudela Formation (Bardenas Reales, Ebro Basin, Spain) and their bearing on the age of the Agenian-Ramblian boundary
FIG. 6. — Ranges of variation and number of specimens for length and width of the upper (pre) molars of Armantomys de Bruijn, 1966 populations of Agenian and Ramblian localities from Spain. BARD (Bardenas) is a combination of the localities published by Murelaga et al. (2004a, b). *, localities described in this paper.
FIG. 1 in Hypsodont Myomiminae (Gliridae, Rodentia) from five new localities in the Lower Miocene Tudela Formation (Bardenas Reales, Ebro Basin, Spain) and their bearing on the age of the Agenian-Ramblian boundary
FIG. 1. — Geological map of the Lower-Middle Miocene continental sediments from the Tudela Formation at the Bardenas Reales de Navarra area, with location of the sections and fossil localities presented in this study. Abbreviations: CA, Cuesta Agujeros section; CC, Cabezo Carboneras section; CM, Cabezo Marijuán section. The location of other fossil localities studied previously by Murelaga (2000) and Murelaga et al. (2004a, b) is also shown.
FIG. 5 in Hypsodont Myomiminae (Gliridae, Rodentia) from five new localities in the Lower Miocene Tudela Formation (Bardenas Reales, Ebro Basin, Spain) and their bearing on the age of the Agenian-Ramblian boundary
FIG. 5. — Ranges of variation and number of specimens for length and width of the lower (pre)molars of Armantomys de Bruijn, 1966 populations of Agenian and Ramblian localities from Spain. BARD (Bardenas) is a combination of the localities published by Murelaga et al. (2004a, b). *, localities described in this paper.
FIG. 2 in Hypsodont Myomiminae (Gliridae, Rodentia) from five new localities in the Lower Miocene Tudela Formation (Bardenas Reales, Ebro Basin, Spain) and their bearing on the age of the Agenian-Ramblian boundary
FIG. 2. — Stratigraphy of the Tudela Formation in the studied sections, with location of the new fossil localities presented here (in bold) and other localities studied previously (Murelaga 2000; Murelaga et al. 2004a, b). The available sequence of polarity intervals identified in the upper part of the Cuesta Agujeros section and the Cabezo Marijuán section and its correlation to the ATNTS of Lourens et al. (2004) is also shown (see Larrasoaña et al. 2006).
FIG. 7 in Hypsodont Myomiminae (Gliridae, Rodentia) from five new localities in the Lower Miocene Tudela Formation (Bardenas Reales, Ebro Basin, Spain) and their bearing on the age of the Agenian-Ramblian boundary
FIG. 7. — Calibration of the Agenian/Ramblian boundary proposed in this study on the basis of the new material from the Tudela For- mation and on the recalibrated radiometric age of the Tardienta fossil locality (Van Dam et al. 2006), placed in the ATNTS of Lourens et al. (2004). The vertical bar indicates the error associated to the 39Ar/40Ar age of the Tardienta fossil locality.
FIG. 4. — A-E in Hypsodont Myomiminae (Gliridae, Rodentia) from five new localities in the Lower Miocene Tudela Formation (Bardenas Reales, Ebro Basin, Spain) and their bearing on the age of the Agenian-Ramblian boundary
FIG. 4. — A-E, Armantomys cf. bijmai from Cabezo Hermoso 1 (CH1): A, left M2, CH1-6; B, right M3, CH1-8; C, right P4, CH1-4; D, right D4, CH1-5; E, right m1, CH1-3; F-M, Armantomys daamsi from Cuesta Agujeros 2 (CA2): F, left M1, CA2/1-6; G, right M2, CA2/1-5; H, right M2, CA2/2-6; I, right M3, CA2/1-15; J, right P4, CA2/1-12; K, left m1, CA2/1-8; L, left m1, CA2/1-10; M, right m3, CA2/2-5; N, Armantomys cf. jasperi from Cuesta Agujeros 4 (CA4), fragmentary right m2, CA4/1-27; O, P, Armantomys cf. parsani from Cuesta Agujeros 3 (CA3): O, left M2, CA3-1; P, left P4, CA3-2; Q, R, Armantomys cf. daamsi from Cabezo Carboneras 1 (CC1): Q, right M1, CC1-31; R, left M2, CC1-38; S, cf. Praearmantomys crusafonti from Cabezo Carboneras 1 (CC1), right m3, CC1-22. Scale bar:1 mm.
Text-fig. 3. m2 and m3 of U. t. mediterraneus from Šandalja I compared with other bear species. a–e: m2, f–g: m3. a: Šandalja I (specimen A; 1 – occlusal, 2 – lingual, 3 – buccal view), b: Šandalja I (specimen B; occlusal view), c: U. t. mediterraneus, Grotta di Reale (IGF 4807V; 1 – occlusal, 2 – lingual view), d: U. etruscus, Olivola (IGF 4605; occlusal view), e: U. etruscus, Upper Valdarno (IGF 908; occlusal view), f: Šandalja I (specimen B; occlusal view), g: U. etruscus, Olivola (IGF 4588; occlusal view). Specimens coated by ammonium chloride; a, b, e, f, g reversed. in Šandalja I (Croatia) And
Text-fig. 3. m2 and m3 of U. t. mediterraneus from Šandalja I compared with other bear species. a–e: m2, f–g: m3. a: Šandalja I (specimen A; 1 – occlusal, 2 – lingual, 3 – buccal view), b: Šandalja I (specimen B; occlusal view), c: U. t. mediterraneus, Grotta di Reale (IGF 4807V; 1 – occlusal, 2 – lingual view), d: U. etruscus, Olivola (IGF 4605; occlusal view), e: U. etruscus, Upper Valdarno (IGF 908; occlusal view), f: Šandalja I (specimen B; occlusal view), g: U. etruscus, Olivola (IGF 4588; occlusal view). Specimens coated by ammonium chloride; a, b, e, f, g reversed.
SASC: A Simple Approach to Synthetic Cohorts. Applying COVID-19 clinical data to generate longitudinal observational patient cohorts and comparison with alternative synthetic cohort approaches as well as real patient data
<p>Subset from COVID-19 Dataset from https://zenodo.org/record/3766350#.YVcfyTFBxgA. Used as reference for a publication dealing with synthetic patient cohort generation.</p>
Dataset for Real and Virtual Backgrounds of Video Calls
<p>Video conferencing applications play an important role in our day-to-day life. They enable people to meet, work, and collaborate remotely, especially in circumstances where physical meetings are not possible (e.g., pandemic scenarios, long distances, etc.). However, such applications might invade people's privacy, such as disclosing their sensitive information. In this dataset, we recorded different video conferencing with diverse real and virtual backgrounds, changing subjects, lighting, and so on.</p> <p>If you want to use these datasets for non-commercial purposes, please cite the following papers:</p> <p>@article{nowroozi2020survey,<br> title={A survey of machine learning techniques in adversarial image forensics},<br> author={Nowroozi, Ehsan and Dehghantanha, Ali and Parizi, Reza M and Choo, Kim-Kwang Raymond},<br> journal={Computers \& Security},<br> pages={102092},<br> year={2020},<br> publisher={Elsevier}<br>}</p> <p> </p>
Synthetic noisy urban soundscapes: a dataset of synthetic soundscapes with real urban backgrounds
<p><strong>Publication</strong></p> <p> </p> <p>If you use this data in your work, please cite the following paper, which introduced this dataset:</p> <p> </p> <p>[1] Pishdadian, F., Wichern, G., & Le Roux, J. (2020). Finding strength in weakness: Learning to separate sounds with weak supervision. IEEE/ACM Transactions on Audio, Speech, and Language Processing (TASLP). [<a href="https://arxiv.org/pdf/1911.02182">pdf</a>]</p> <p>[2] Cramer, A., Cartwright, M., Pishdadian, F., and Bello, J.P. Weakly Supervised Source-Specific Sound Level Estimation in Noisy Soundscapes. In Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2021. [<a href="https://arxiv.org/pdf/2105.02911">pdf</a>]</p> <p><br> <strong>Created by</strong></p> <p>Fatemeh Pishdadian (1), Gordon Wichern (2), Jonathan Le Roux (2), Aurora Cramer (3, 4), Mark Cartwright (5), and Juan Pablo Bello (3,4,6,7)</p> <p> 1. Interactive Audio Lab, Northwestern University<br> 2. Mitsubishi Electric Research Laboratory<br> 3. Music and Audio Research Lab, New York University<br> 4. Department of Electrical and Computer Engineering, New York University<br> 5. Department of Informatics, New Jersey Institute of Technology<br> 6. Center for Urban Science and Progress, New York University<br> 7. Department of Computer Science and Engineering, New York University</p> <p><br> <strong>Description</strong></p> <p>Synthetic noisy urban soundscapes (SNUSS) is a dataset of synthetic soundscapes with real urban background noise meant to mimic urban soundscapes. This dataset contains 30,000 10 second mixtures, their isolated components, and auto-generated annotations. This dataset was developed with the goal of synthesizing soundscapes with a diverse set of realistic sounding background activity, for use in developing and evaluating machine listening systems in urban settings.</p> <p><br> <strong>Mixture generation</strong></p> <p>We generate synthetic mixtures using a collection of isolated sound events with class annotations, as well as a collection of urban background noise. Audio mixtures are 4 seconds long (at 16kHz). We generate a foreground sub-mixture using a subset of clips from <a href="https://urbansounddataset.weebly.com/urbansound8k.html">UrbanSound8K</a> [3] from the <em>car horn</em>, <em>dog bark</em>, <em>gun shot</em>, <em>jackhammer</em> and <em>siren</em> classes. These clips range from 0.5 s to 4s. The number of events per mixture is sampled from a zero-truncated Poisson distribution with a rate parameter of 5. The class for each event is chosen uniformly at random from the five target classes. The particular sound event is chosen uniformly at random from the available clips for that class. The start time is chosen uniformly throughout the clip such that the entire clip is contained in the 4 second mixture. A brief fade-in and fade-out is applied to the clip to avoid discontinuities. Each clip is set to a sound level sampled uniformly at random in the range -30 to -20 dB LUFS.</p> <p>For the background audio, we use urban background recordings from the SONYC-Background dataset [2, 4], containing 441 10 second recordings of urban background noise in New York City. For more information, see the <a href="https://doi.org/10.5281/zenodo.5129078">SONYC-Backgrounds page</a>. For each mixture, a random background clip is chosen from which we extract a uniformly chosen 4 second segment.</p> <p>We create datasets using an foreground-to-background SNRs of -50, -20 -0 dB LUFS, (`n50dB`, `n20dB`, and `0dB` respectively), in addition to a noiseless dataset (`none`). The datasets are generated such that the only difference between them is the relative loudness between the foreground and background.</p> <p>For the training set, we generate 20,000 mixtures using folds 1-6 of UrbanSound8K and the training set of SONYC-Background. For the validation set, we generate 5,000 examples using folds 7-8 of UrbanSound8K and the validation set of SONYC-Background. For the test set, we generate 5,000 examples using folds 9-10 of UrbanSound8K and the test set of SONYC-Background.</p> <p>For additional details on the foreground mixture generation process, please refer to [1]. For additional details on generating the soundscapes with background, please refer to [2].</p> <p><br> <strong>Files</strong></p> <p>The dataset files are split into the following compressed archives:</p> <ul> <li>`synthetic-noisy-urban-soundscapes_mixtures-bkgr-none.tar.gz` - Noiseless mixtures</li> <li>`synthetic-noisy-urban-soundscapes_mixtures-bkgr-n50dB.tar.gz` - Mixtures with -50 dB LUFS SNR</li> <li>`synthetic-noisy-urban-soundscapes_mixtures-bkgr-n20dB.tar.gz` - Mixtures with -20 dB LUFS SNR</li> <li>`synthetic-noisy-urban-soundscapes_mixtures-bkgr-0dB.tar.gz` - Mixtures with 0 dB LUFS SNR</li> <li>`synthetic-noisy-urban-soundscapes_isolated_events.tar.gz` - Isolated sound events for each mixture</li> </ul> <p><br> <em>Preparing the files</em></p> <ol> <li>Download each of the tar.gz files to a new folder. You need at the `isolated_events` and one of the mixture datasets.</li> <li>Decompress all of the tar.gz files.</li> <li>Merge the contents of the extracted `isolated_events` folder into the extracted mixture folders. This makes sure the corresponding isolated events for each mixture are placed in its `XXXXX_events` folder.</li> </ol> <p><br> <em>File structure</em></p> <p>The mixture dataset folder for the desired background condition should have the format `synthetic-noisy-urban-soundscapes_mixtures-bkgr-<condition>/<split>`. Within each split folder are the mixture files, which are identified by an integer (padded with leading zeros up to 5 places). For a mixture `00001`, the mixture audio is `00001.wav` and the annotation file (in JAMS [5] format) is `00001.jams`. The isolated events can be found in the `00001_events` folder, where foreground events have the format `foreground<fg-event-num>_<class-name>.wav` and the background recording (if used) is called `background0_2017.wav`.</p> <p><br> <strong>Contact</strong></p> <p>If you have any questions, comments, or concerns, please direct correspondence to Aurora Cramer (aurora (dot) linh (dot) cramer (at) gmail (dot) com).</p> <p> </p> <p><strong>References</strong></p> <p>[1] Pishdadian, F., Wichern, G., & Le Roux, J. (2020). Finding strength in weakness: Learning to separate sounds with weak supervision. IEEE/ACM Transactions on Audio, Speech, and Language Processing (TASLP).</p> <p>[2] Cramer, A., Cartwright, M., Pishdadian, F., and Bello, J. P. (2021). Weakly Supervised Source-Specific Sound Level Estimation in Noisy Soundscapes. In 2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA).</p> <p>[3] Salamon, J., Jacoby, C., and Bello, J.P. (2014). A dataset and taxonomy for urban sound research. In 2014 ACM International Conference on Multimedia.</p> <p>[4] Cramer, A., Cartwright, M., Pishdadian, F., and Bello, J.P. (2021). SONYC-Backgrounds: a collection of urban background recordings from an acoustic sensor network (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.5129078</p> <p>[5] Humphrey, E. J., Salamon, J., Nieto, O., Forsyth, J., Bittner, R. M., and Bello, J.P. (2014). JAMS: A JSON Annotated Music Specification for Reproducible MIR Research. In 2014 International Society for Music Information Retrieval Conference (ISMIR)</p> <p><br> <strong>Acknowledgements</strong></p> <p>This work is partially supported by National Science Foundation <a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=1633259">award 1633259</a> and <a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=1544753">award 1544753</a>.</p> <p> </p>
ScienceDex guides
Understand access before you commit
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