Find research datasets worth reusing

Figure 2 from: Sampaio Í, Carreiro-Silva M, Freiwald A, Menezes G, Grasshoff M (2019) Natural history collections as a basis for sound biodiversity assessments: Plexauridae (Octocorallia, Holaxonia) of the Naturalis CANCAP and Tyro Mauritania II expeditions. ZooKeys 870: 1-32. https://doi.org/10.3897/zookeys.870.35285

Figure 2 CANCAP and Tyro Mauritania II Plexauridae collection: a label of a catalogued record of Thesea talismani in Mauritania b label of previously unidentified and uncatalogued record from the Azores archipelago c different colonies of a new species from a coral garden filmed in situ in 2016 but stored in NBC since 1988 d cf. Placogorgia sp. I (RMNH. COEL. 42336) found mixed with specimens of the primnoid Callogorgia verticillata (Pallas, 1766) e the putative new record of Paramuricea macrospina in the NE Atlantic Ocean f detail of a new record for the Azores archipelago, Paramuricea biscaya (RMNH. COEL. 423339). Scale bars: 1 cm.

Figure 1 from: Sampaio Í, Carreiro-Silva M, Freiwald A, Menezes G, Grasshoff M (2019) Natural history collections as a basis for sound biodiversity assessments: Plexauridae (Octocorallia, Holaxonia) of the Naturalis CANCAP and Tyro Mauritania II expeditions. ZooKeys 870: 1-32. https://doi.org/10.3897/zookeys.870.35285

Figure 1 The National Museum of Natural History of The Netherlands. Naturalis Biodiversity Center in 2016 (a), Marine Invertebrate Collection (b), part of the CANCAP and Tyro Mauritania II consulted collection (c, d).

Figure 5 from: Sampaio Í, Carreiro-Silva M, Freiwald A, Menezes G, Grasshoff M (2019) Natural history collections as a basis for sound biodiversity assessments: Plexauridae (Octocorallia, Holaxonia) of the Naturalis CANCAP and Tyro Mauritania II expeditions. ZooKeys 870: 1-32. https://doi.org/10.3897/zookeys.870.35285

Figure 5 Bathymetric distribution of Plexauridae identified to species at different sampling stations of CANCAP and Tyro Mauritania II cruises on the NE Atlantic Ocean. Symbols represent precise records while bars represent distribution ranges. Colours represent distinct regions: Azores (blue), Madeira (yellow), Selvagens (white), Canary Islands (orange), Cape Verde (green) and Mauritania (rose).

Figure 3 from: Sampaio Í, Carreiro-Silva M, Freiwald A, Menezes G, Grasshoff M (2019) Natural history collections as a basis for sound biodiversity assessments: Plexauridae (Octocorallia, Holaxonia) of the Naturalis CANCAP and Tyro Mauritania II expeditions. ZooKeys 870: 1-32. https://doi.org/10.3897/zookeys.870.35285

Figure 3 Map of Plexauridae collected during CANCAP and Tyro II Mauritania cruises except uncertain Cape Verdean records (see Figure 4).

Spanish Antarctica campaign 2018/2019 ionosphere sounding dataset Caleta Argentina

This data set contains the IQ data files acquired in Caleta Argentina during the Spanish Antarctica campaign from 4-2-2019 at 15:07 UTC from, the 8-2-2019 at 12:59 UTC from the founded project by  CTM2015-68902-R (MINECO/FEDE). This data are the ionosphere sensing data, the sensing channel is realized by modifying the power transmission, channel BW, and the modulation (from 2QAM to 32QAM, 2PSK to 32PSK and 2FSK to 32FSK).

opencc-by-4.0Aug 2024View details →

Anthropomorphic sound study-- sound files

<h1>Sound files from Sonic Sentience? Sound’s Influence on Perceptions of Anthropomorphism and Animacy  </h1> These sound files were used to study the role of anthropomorphism and animacy in simple parking meter animation. The robot sounds were described as the "cute" sounds in the study and were created by PDX Drescher. The others are from the Google Material Design interface sounds.

opencc-by-4.0Sep 2020View details →

Figures 3a-c in Bioacoustic study of a sound produced with forced air by larvae of Phileurus didymus (Linnaeus) (Coleoptera: Scarabaeidae: Dynastinae: Phileurini)

Figures 3a-c. Oscillograms of acoustic patterns of Phileurus didymus. (a) Larvae Non-constant, semirepeating short pulses, note that interpulses (space between pulses) vary. (b) Slightly repetitive pulses with variable sequences (the interpulses varies a little). (c) Short pulses with long duration pulses.

opencc-by-4.0Sep 2020View details →

Figures 2a-d in Bioacoustic study of a sound produced with forced air by larvae of Phileurus didymus (Linnaeus) (Coleoptera: Scarabaeidae: Dynastinae: Phileurini)

Figures 2a-d. Techniques for the observation of responses of the larvae to different disturbances. a) Disturbance with a wooden rod. b) Direct disturbance to the container. c) Response to disturbance close to the larva (Larva-larva interaction). d) Open-air exposure of larvae.

opennotspecifiedAug 2024View details →

Thomas J. Walker Sound Recordings from Macaulay Library of Natural Sounds: Thomas J. Walker__s insect recordings

This resource includes a large selection of the recordings deposited in the Macaulay Library of Natural Sounds by Thomas J. Walker. These recordings are mainly calling songs of crickets and katydids from North America north of Mexico and from the Caribbean.

opencc-by-nc-4.0Oct 2024View details →

Soundscape Datasets for Few-Shot Bird Sound Classification

This repository provides easy access to open-source soundscape datasets of bird sounds, specifically optimized for few-shot classification. <code>soundscapes.zip</code> contains evaluation soundscape datasets from the BIRB benchmark (https://arxiv.org/abs/2312.07439), downsampled to 16kHz, preprocessed using CNN14 from PANNs (https://arxiv.org/abs/1912.10211), to select a 6-second window with the highest bird activation, and converted to Pytorch (.pt) format to facilitate usability for evaluating deep neural networks.  These preprocessed datasets are employed in the work "Domain-Invariant Representation Learning of Bird Sounds" (https://arxiv.org/abs/2409.08589), which evaluates the few-shot learning capabilities of deep learning models trained on focal recordings (e.g., Xeno-Canto) and tested on soundscape recordings. <h2>Dataset Structure</h2> <h3>Validation Dataset</h3> <ul> <li>POW (<code>pow.pt</code>): The validation dataset consists of 16,047 examples across 43 classes and is organized as a dictionary with <code>'data'</code> and<code> 'label'</code> keys representing bird sounds and their corresponding labels. Storing the entire validation dataset in a single tensor enables rapid loading and efficient processing, significantly accelerating the validation process. Classes with only one example are removed, as they are insufficient for one-shot classification tasks. Source: https://zenodo.org/records/4656848#.Y7ijhOxudhE</li> </ul> <h3>Test Datasets </h3> Each test dataset is structured with multiple subfolders, each labeled with an eBird species code to represent data for a specific bird species. <ul> <li>SSW (<code>ssw/</code>): Contains 50,760 examples across 96 classes. Source: https://zenodo.org/records/7079380#.Y7ijHOxudhE</li> <li>NES (<code>coffee_farms/</code>): Contains 6,952 examples across 89 classes. Source: https://zenodo.org/records/7525349#.ZB8z_-xudhE</li> <li>UHH (<code>hawaii/</code>): Contains 59,583 examples across 27 classes. Source: https://zenodo.org/records/7078499#.Y7ijPuxudhE</li> <li>HSN (<code>high_sierras/</code>): Contains 10,296 examples across 19 classes. Source: https://zenodo.org/records/7525805#.ZB8zsexudhE</li> <li>SNE (<code>sierras_kahl/</code>): Contains 20,147 examples across 56 classes. Source: https://zenodo.org/records/7050014#.Y7ijWexudhE</li> <li>PER (<code>peru/</code>): Contains 14,768 examples across 132 classes. Source: https://zenodo.org/records/7079124#.Y7iis-xudhE</li> </ul> Code and detailed instructions, including data loading, model implementation, and few-shot evaluation, can be found at: https://github.com/ilyassmoummad/ProtoCLR

opencc-by-4.0Oct 2024View details →

European Countries financial soundness data

European Countries financial soundness data

opencc-by-4.0Aug 2024View details →

WildDESED: An LLM-Powered Dataset for Wild Domestic Environment Sound Event Detection System

A new large language model (LLM)-powered dataset namely wild domestic environment sound event detection (WildDESED). It is crafted as an extension to the original DESED dataset to reflect diverse acoustic variability and complex noises in home settings. We leveraged LLMs to generate eight different domestic scenarios based on target sound categories of the DESED dataset. Then we enriched the scenarios with a carefully tailored mixture of noises selected from AudioSet and ensured no overlap with target sound. We consider widely popular convolutional neural recurrent network to study WildDESED dataset, which depicts its challenging nature. We then apply curriculum learning by gradually increasing noise complexity to enhance the model's generalization capabilities across various noise levels.

opencc-by-4.0Nov 2024View details →

compression and rarefaction of sound

SciDraw upload

restrictedcc-by-4.0Nov 2024View details →

Filming the sound: Anomaly Detection on Audio Tape Recordings using Computer Vision Algorithms

This repository makes available the dataset related to the paper: Zafer Çınar, Alessandro Russo, Matteo Spanio, Niccolò Pretto, and Sergio Canazza, Filming the Sound: Anomaly Detection on Audio Tape Recordings using Computer Vision Algorithms, IAI4CH, Bozen, 2024. The dataset and the experiment are described in the publication above. This repository contains two main directories (bold indicates directory names): <ul> <li>video samples: the actual videos used in the paper's experiment. This folder contains four subdirectories - 3.75 ips, 7.5 ips, 15 ips, and 30 ips - each representing a different playback speed (in inches per second). Within each subdirectory are several MP4 files, recorded on an A810 Studer open reel recorder, documenting the playback of magnetic audio tapes. The files follow the naming convention “Xips (Y).mp4,” where X represents the tape playback speed and Y is a serial number identifier for each video.</li> <li>irregularities: the metadata for each video with timestamp and type of irregularity. The folder includes four CSV files - 3.75.csv, 7.5.csv, 15.csv, and 30.csv - corresponding to the playback speeds of the video samples. Each CSV file provides handmade annotations for its respective videos, with three columns: <ul> <li>video_id: name of the video file in the format “Xips (Y).mp4,” where X is the tape speed and Y is the ID number.</li> <li>time_label: timestamp indicating the irregularity, formatted as HH:MM:SS.mls.</li> <li>irregularity_type: category of the detected anomaly, which may be one of the following: “splice,” “shadow,” “end-of-tape,” or “annotation.”</li> </ul> </li> </ul>

dryad28/100

Data from: Reconstructing the spectrotemporal modulations of real-life sounds from fMRI response patterns

Ethological views of brain functioning suggest that sound representations and computations in the auditory neural system are optimized finely to process and discriminate behaviorally relevant acoustic features and sounds (e.g., spectrotemporal modulations in the songs of zebra finches). Here, we show that modeling of neural sound representations in terms of frequency-specific spectrotemporal modulations enables accurate and specific reconstruction of real-life sounds from high-resolution functional magnetic resonance imaging (fMRI) response patterns in the human auditory cortex. Region-based analyses indicated that response patterns in separate portions of the auditory cortex are informative of distinctive sets of spectrotemporal modulations. Most relevantly, results revealed that in early auditory regions, and progressively more in surrounding regions, temporal modulations in a range relevant for speech analysis (∼2–4 Hz) were reconstructed more faithfully than other temporal modulations. In early auditory regions, this effect was frequency-dependent and only present for lower frequencies (<∼2 kHz), whereas for higher frequencies, reconstruction accuracy was higher for faster temporal modulations. Further analyses suggested that auditory cortical processing optimized for the fine-grained discrimination of speech and vocal sounds underlies this enhanced reconstruction accuracy. In sum, the present study introduces an approach to embed models of neural sound representations in the analysis of fMRI response patterns. Furthermore, it reveals that, in the human brain, even general purpose and fundamental neural processing mechanisms are shaped by the physical features of real-world stimuli that are most relevant for behavior (i.e., speech, voice).

opencc-zeroDec 2016View details →

opencc-by-4.0Jul 2021View details →

Audio-visual sound source localization and separation

CVPR 2021 tutorial

Figure 10 in Do small ermine moths sing? Possible stridulatory sound production in Yponomeutidae (Lepidoptera)

Figure 10. Hind leg of Y. padella.

Figure 7 in Do small ermine moths sing? Possible stridulatory sound production in Yponomeutidae (Lepidoptera)

Figure 7. Confocal image of the stridularium of Y. paurodes (Australia).

Figure 5 in Do small ermine moths sing? Possible stridulatory sound production in Yponomeutidae (Lepidoptera)

Figure 5. Confocal image of the stridularium of Y. fumigata (Africa).

Figure 4 in Do small ermine moths sing? Possible stridulatory sound production in Yponomeutidae (Lepidoptera)

Figure 4. Detail of part of the stridularium of Yponomeuta padella.