19,997 machine learning datasets
19,997 dataset results
CLO-43SD is a dataset for multi-class species identification in avian flight calls. It consists of 5,428 labeled audio clips of flight calls from 43 different species of North American woodwarblers (in the family Parulidae). The clips came from a variety of recording conditions, including clean recordings obtained using highly-directional shotgun microphones, recordings obtained from noisier field recordings using omnidirectional microphones, and recordings obtained from birds in captivity.
CLO-WTSP is a dataset for species-specific flight call identification for the White-Throated Sparrow. 16,703 labeled audio clips captured by remote acoustic sensors deployed in Ithaca, NY and NYC over the fall 2014 and spring 2015 migration seasons. Each clip is labeled to indicate whether it contains a flight call from the target species White-Throated Sparrow (WTSP), a flight call from a non-target species, or no flight call at all.
CLO-SWTH is a dataset for species-specific flight call identification for the Swainson’s Thrush. 179,111 labeled audio clips captured by remote acoustic sensors deployed in Ithaca, NY and NYC over the fall 2014 and spring 2015 migration seasons. Each clip is labeled to indicate whether it contains a flight call from the target species Swainson’s Thrush (SWTH), a flight call from a non-target species, or no flight call at all.
Freefield1010 is a collection of 7,690 excerpts from field recordings around the world, gathered by the FreeSound project, and then standardised for research.
warblrb10k is a collection of 10,000 smartphone audio recordings from around the UK, crowdsourced by users of Warblr the bird recognition app. The audio covers a wide distribution of UK locations and environments, and includes weather noise, traffic noise, human speech and even human bird imitations.
Chernobyl is a collection of 620 audio clips collected from unattended remote monitoring equipment in the Chernobyl Exclusion Zone (CEZ). This data was collected as part of the TREE (Transfer-Exposure-Effects) research project into the long-term effects of the Chernobyl accident on local ecology. The audio covers a range of birds and includes weather, large mammal and insect noise sampled across various CEZ environments, including abandoned village, grassland and forest areas.
PolandNFC is a collection of 4,000 recordings from Hanna Pamuła's PhD project of monitoring autumn nocturnal bird migration. The recordings were collected every night, from September to November 2016 on the Baltic Sea coast, Poland, using Song Meter SM2 units with microphones mounted on 3–5 m poles. A subset derived from 15 nights with different weather conditions and background noise including wind, rain, sea noise, insect calls, human voice and deer calls was used in DCASE 2018 Challenge.
The BirdVox-DCASE-20k dataset contains 20,000 ten-second audio recordings. These recordings come from ROBIN autonomous recording units, placed near Ithaca, NY, USA during the fall 2015. They were captured on the night of September 23rd, 2015, by six different sensors, originally numbered 1, 2, 3, 5, 7, and 10. Out of these 20,000 recording, 10,017 (50.09%) contain at least one bird vocalization (either song, call, or chatter). The dataset is a derivative work of the BirdVox-full-night dataset, containing almost as much data but formatted into ten-second excerpts rather than ten-hour full night recordings.
DBR dataset is an environmental audio dataset created for the Bachelor's Seminar in Signal Processing in Tampere University of Technology. The samples in the dataset were collected from the online audio database Freesound. The dataset consists of three classes, each containing 50 samples, and the classes are 'dog', 'bird', and 'rain' (hence the name DBR).
The FSL4 dataset contains ~4000 user-contributed loops uploaded to Freesound. Loops were selected by searching Freesound for sounds with the query terms loop and bpm, and then automatically parsing the returned sound filenames, tags and textual descriptions to identify tempo annotations made by users. For example, a sound containing the tag 120bpm is considered to have a ground truth of 120 BPM.
SimSceneTVB is a dataset of 600 simulated sound scenes of 45s each representing urban sound environments, simulated using the simScene Matlab library. The dataset is divided in two parts with a train subset (400 scenes) and a test subset (200 scenes) for the development of learning-based models. Each scene is composed of three main sources (traffic, human voices and birds) according to an original scenario, which is composed semi-randomly conditionally to five ambiances: park, quiet street, noisy street, very noisy street and square. Separate channels for the contribution of each source are available. The base audio files used for simulation are obtained from Freesound (https://freesound.org) and LibriSpeech (http://www.openslr.org/12). The sound scenes are scaled according to a playback sound level in dB, which is drawn randomly but remains plausible according to the ambiance.
SimSceneTVB Perception is a corpus of 100 sound scenes of 45s each representing urban sound environments, including: 6 scenes recorded in Paris, 19 scenes simulated using simScene to replicate recorded scenarios, 75 scenes simulated using simScene with diverse new scenarios, containing traffic, human voices and bird sources.The base audio files used for simulation are obtained from Freesound (https://freesound.org) and LibriSpeech (http://www.openslr.org/12).
The Sound Events for Surveillance Applications (SESA) dataset files were obtained from Freesound. The dataset was divided between train (480 files) and test (105 files) folders. All audio files are WAV, Mono-Channel, 16 kHz, and 8-bit with up to 33 seconds. # Classes: 0 - Casual (not a threat) 1 - Gunshot 2 - Explosion 3 - Siren (also contains alarms).
TUT Rare Sound events 2017, development dataset consists of source files for creating mixtures of rare sound events (classes baby cry, gun shot, glass break) with background audio, as well a set of readily generated mixtures and recipes for generating them. The "source" part of the dataset consists of two subsets: (a) background recordings from 15 different acoustic scenes, (b) recordings with the target rare sound events from three classes, accompanied by annotations of their temporal occurrences, (c) a set of meta files providing the cross-validation setup: lists of background and target event recordings split into training and test subsets (called "devtrain" and "devtest", respectively, indicating they are provided as the development dataset, as opposed to the evaluation dataset released separately). The mixture set consists of two subsets (training and testing), each containing ~1500 mixtures (~500 per target class in each subset, with half of the mixtures not containing any target
The TUT Sounds Event 2018 dataset consists of real-life first order Ambisonic (FOA) format recordings with stationary point sources each associated with a spatial coordinate. The dataset was generated by collecting impulse responses (IR) from a real environment using the Eigenmike spherical microphone array. The measurement was done by slowly moving a Genelec G Two loudspeaker continuously playing a maximum length sequence around the array in circular trajectory in one elevation at a time. The playback volume was set to be 30 dB greater than the ambient sound level. The recording was done in a corridor inside the university with classrooms around it during work hours. The IRs were collected at elevations −40 to 40 with 10-degree increments at 1 m from the Eigenmike and at elevations −20 to 20 with 10-degree increments at 2 m.
NeuB1 is a microscopic neuronal image dataset for retinal vessel segmentation, which contains 112 images of size 512 x 152. The train/test split is 37/75. Image Source: https://web.bii.a-star.edu.sg/~zhaoh/Jaydeep_Tracing/
DementiaBank is a shared database of multimedia interactions for the study of communication in dementia. The dataset contains 117 people diagnosed with Alzheimer Disease, and 93 healthy people, reading a description of an image. The principal task and benchmark is to classify each group.
Yesno is an audio dataset consisting of 60 recordings of one individual saying yes or no in Hebrew; each recording is eight words long. It was created for the Kaldi audio project by an author who wishes to remain anonymous.
iQIYI-VID-2019 dataset is the first video dataset for multi-model person identification. This dataset aims to encourage the research of multi-modal based person identification. To get close to real applications, video clips are extracted from real online videos of extensive types. All the clips are labeled by human annotators, and use automatic algorithms to accelerate the collection and labeling process. The iQIYI-VID-2019 dataset is more challenging comparing to the iQIYI-VID-2018 dataset, since most hard examples are selected from iQIYI-VID-2018 while more person ids is added. The dataset contains 100K~200K video clips, divided into three parts, 40% for training, 30% for validation, and 30% for test. The dataset contains about 10, 000 identities, 5,000 of which come from the iQIYI celebrity database and mainly extracts from iQIYI-VID-2018.
Blind Audio-Visual Localization (BAVL) Dataset consists of 20 audio-visual recordings of sound sources, which could be talking faces or music instruments. Most audio-visual recordings (19) are videos from Youtube except V8, which is from 1. Besides, the video V7 was also used in2, and V16 used in 3. All 20 videos are annotated by ourselves in a uniform manner. Details of the video sequences are listed in Table 1.