Omkar2118/datasets huggingface dataset:
DSTC7 Task 2
Full name: Dialog System Technology Challenges Task 2

DSTC Task 2 is a dataset and task for end-to-end conversation modeling. The goal is to generate conversational responses that go beyond trivial chitchat by injecting informative responses that are grounded in external knowledge. The data consists of conversational data from Reddit, and contextually-relevant “facts” taken from the website that started the Reddit conversation. That is the setup is grounded, as each conversation in the data is about a specific web page that was linked at the start of the conversation.

Source: [http://workshop.colips.org/dstc7/](http://workshop.colips.org/dstc7/)
Image Source: [http://workshop.colips.org/dstc7/](http://workshop.colips.org/dstc7/)
DCASE 2013
Full name: DCASE 2013

**DCASE 2013** is a dataset for sound event detection. It consists of audio-only recordings where individual sound events are prominent in an acoustic scene.

Source: [http://dcase.community/challenge2013/index](http://dcase.community/challenge2013/index)
Image Source: [https://link.springer.com/article/10.1186/s13636-018-0138-4](https://link.springer.com/article/10.1186/s13636-018-0138-4)

Introducing paper: Detection and Classification of Acoustic Scenes and Events
LITIS Rouen
Full name: LITIS Rouen

The LITIS-Rouen dataset  is a dataset for audio scenes. It consists of 3026 examples of 19 scene categories. Each class is specific to a location such as a train station or an open market. The audio recordings have a duration of 30 seconds and a sampling rate of 22050 Hz. The dataset has a total duration of 1500 minutes.

Source: [Spatio-Temporal Attention Pooling for Audio Scene Classification](https://arxiv.org/abs/1904.03543)
Image Source: [https://www.researchgate.net/figure/Summary-of-Litis-Rouen-audio-scene-dataset_tbl1_329608235](https://www.researchgate.net/figure/Summary-of-Litis-Rouen-audio-scene-dataset_tbl1_329608235)

Introducing paper: Histogram of Gradients of Time-Frequency Representations for Audio Scene Classification
YouTube-100M
Full name: YouTube-100m

The **YouTube-100M** data set consists of 100 million YouTube videos: 70M training videos, 10M evaluation videos, and 20M validation videos. Videos average 4.6 minutes each for a total of 5.4M training hours. Each of these videos is labeled with 1 or more topic identifiers from a set of 30,871 labels. There are an average of around 5 labels per video. The labels are assigned automatically based on a combination of metadata (title, description, comments, etc.), context, and image content for each video. The labels apply to the entire video and range from very generic (e.g. “Song”) to very specific (e.g. “Cormorant”).
Being machine generated, the labels are not 100% accurate and of the 30K labels, some are clearly acoustically relevant (“Trumpet”) and others are less so (“Web Page”). Videos often bear annotations with multiple degrees of specificity. For example, videos labeled with “Trumpet” are often labeled “Entertainment” as well, although no hierarchy is enforced.

Source: [https://arxiv.org/pdf/1609.09430.pdf](https://arxiv.org/pdf/1609.09430.pdf)
Image Source: [https://arxiv.org/pdf/1609.09430.pdf](https://arxiv.org/pdf/1609.09430.pdf)

Introducing paper: CNN Architectures for Large-Scale Audio Classification
FSDnoisy18k
Full name: FSDnoisy18k

The **FSDnoisy18k** dataset is an open dataset containing 42.5 hours of audio across 20 sound event classes, including a small amount of manually-labeled data and a larger quantity of real-world noisy data. The audio content is taken from Freesound, and the dataset was curated using the Freesound Annotator. The noisy set of FSDnoisy18k consists of 15,813 audio clips (38.8h), and the test set consists of 947 audio clips (1.4h) with correct labels. The dataset features two main types of label noise: in-vocabulary (IV) and out-of-vocabulary (OOV). IV applies when, given an observed label that is incorrect or incomplete, the true or missing label is part of the target class set. Analogously, OOV means that the true or missing label is not covered by those 20 classes.

Source: [Model-agnostic Approaches to Handling Noisy Labels When Training Sound Event Classifiers](https://arxiv.org/abs/1910.12004)
Image Source: [http://www.eduardofonseca.net/FSDnoisy18k/](http://www.eduardofonseca.net/FSDnoisy18k/)

Introducing paper: Learning Sound Event Classifiers from Web Audio with Noisy Labels
CHiME-Home
Full name: CHiME-Home

**CHiME-Home** is a dataset for sound source recognition in a domestic environment. It uses around 6.8 hours of domestic environment audio recordings. The recordings were obtained from the CHiME projects – computational hearing in multisource environments – where recording equipment was positioned inside an English Victorian semi-detached house. The recordings were selected from 22 sessions totalling 19.5 hours, with each session made between 7:30 in the morning and 20:00 in the evening. In the considered recordings, the equipment was placed in the lounge (sitting room) near the door opening onto a hallway, with the hallway opening onto a kitchen with no door. With the lounge door typically open, prominent sounds thus may originate from sources both in the lounge and kitchen.

The choice of permitted labels was motivated by the sources present in the considered acoustic environment: Human speakers (c,m,f); human activity (p); television (v); household appliances (b). Further labels o,S,U respectively relate to any other identifiable sounds, silence, unidentifiable sounds. Labels S,U may respectively only be assigned in isolation. Annotators were acquired to assign at least one label to a chunk, thus annotators may either assign one or more labels from the set {c,m,f,v,p,b,o}, or may alternatively ‘flag’ the chunk using a single label from the set {S,U}.

Source: [https://www.researchgate.net/publication/308732345_CHiME-Home_A_dataset_for_sound_source_recognition_in_a_domestic_environment](https://www.researchgate.net/publication/308732345_CHiME-Home_A_dataset_for_sound_source_recognition_in_a_domestic_environment)
Image Source: [https://www.researchgate.net/publication/308732345_CHiME-Home_A_dataset_for_sound_source_recognition_in_a_domestic_environment](https://www.researchgate.net/publication/308732345_CHiME-Home_A_dataset_for_sound_source_recognition_in_a_domestic_environment)

Introducing paper: Chime-home: A dataset for sound source recognition in a domestic environment
Bach Chorales
Full name: Bach Chorales

Bach chorales is a univariate time series based on chorales, where the task is to learn generative grammar. The dataset consists of single-line melodies of 100 Bach chorales (originally 4 voices). The melody line can be studied independently of other voices. The grand challenge is to learn a generative grammar for stylistically valid chorales.

Source: [https://archive.ics.uci.edu/ml/datasets/Bach+Chorales](https://archive.ics.uci.edu/ml/datasets/Bach+Chorales)
Image Source: [https://arxiv.org/pdf/1612.01010.pdf](https://arxiv.org/pdf/1612.01010.pdf)
FAIR-Play
Full name: FAIR-Play

**FAIR-Play** is a video-audio dataset consisting of 1,871 video clips and their corresponding binaural audio clips recording in a music room. The video clip and binaural clip of the same index are roughly aligned.

Source: [https://github.com/facebookresearch/FAIR-Play](https://github.com/facebookresearch/FAIR-Play)
Image Source: [https://github.com/facebookresearch/FAIR-Play](https://github.com/facebookresearch/FAIR-Play)

Introducing paper: 2.5D Visual Sound
BirdVox-full-night
Full name: BirdVox-full-night

The **BirdVox-full-night** dataset contains 6 audio recordings, each about ten hours in duration. 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.
Andrew Farnsworth used the Raven software to pinpoint every avian flight call in time and frequency. He found 35402 flight calls in total. He estimates that about 25 different species of passerines (thrushes, warblers, and sparrows) are present in this recording. Species are not labeled in BirdVox-full-night, but it is possible to tell apart thrushes from warblers and sparrrows by looking at the center frequencies of their calls. The annotation process took 102 hours.

Source: [https://wp.nyu.edu/birdvox/birdvox-full-night/](https://wp.nyu.edu/birdvox/birdvox-full-night/)
Image Source: [https://wp.nyu.edu/birdvox/birdvox-full-night/](https://wp.nyu.edu/birdvox/birdvox-full-night/)

Introducing paper: Birdvox-Full-Night: A Dataset and Benchmark for Avian Flight Call Detection
POP909
Full name: POP909

**POP909** is a dataset which contains multiple versions of the piano arrangements of 909 popular songs created by professional musicians. The main body of the dataset contains the vocal melody, the lead instrument melody, and the piano accompaniment for each song in MIDI format, which are aligned to the original audio files. Furthermore, annotations are provided of tempo, beat, key, and chords, where the tempo curves are hand-labelled and others are done by MIR algorithms.

Source: [https://arxiv.org/pdf/2008.07142.pdf](https://arxiv.org/pdf/2008.07142.pdf)
Image Source: [https://arxiv.org/pdf/2008.07142.pdf](https://arxiv.org/pdf/2008.07142.pdf)

Introducing paper: POP909: A Pop-song Dataset for Music Arrangement Generation
SINS
Full name: SINS

**SINS** is a database of continuous real-life audio recordings in a home environment. The home is a vacation home and one person lived there during the recording period of over on week. It was collected using a network of 13 microphone arrays distributed over the multiple rooms. Each microphone array consisted of 4 linearly arranged microphones. Recordings were annotated based on the level of daily activities performed in the environment.

Source: [https://www.cs.tut.fi/sgn/arg/dcase2017/documents/workshop_papers/DCASE2017Workshop_Dekkers_141.pdf](https://www.cs.tut.fi/sgn/arg/dcase2017/documents/workshop_papers/DCASE2017Workshop_Dekkers_141.pdf)
Image Source: [https://www.cs.tut.fi/sgn/arg/dcase2017/documents/workshop_papers/DCASE2017Workshop_Dekkers_141.pdf](https://www.cs.tut.fi/sgn/arg/dcase2017/documents/workshop_papers/DCASE2017Workshop_Dekkers_141.pdf)
Robbie Williams
Full name: Robbie Williams

**Robbie Williams** is a dataset of 65 songs by Robbie Williams. It consists of chords, keys and beats. The dataset does not include audio.

Source: [A BI-DIRECTIONAL TRANSFORMER FOR MUSICAL CHORD RECOGNITION](https://arxiv.org/abs/1907.02698)
Image Source: [https://www.rwdb.info/](https://www.rwdb.info/)
URBAN-SED
Full name: URBAN-SED

**URBAN-SED** is a dataset of 10,000 soundscapes with sound event annotations generated using the scraper library. The dataset includes 10,000 soundscapes, totals almost 30 hours and includes close to 50,000 annotated sound events. Every soundscape is 10 seconds long and has a background of Brownian noise resembling the typical “hum” often heard in urban environments. Every soundscape contains between 1-9 sound evnts from the following classes: air_conditioner, car_horn, children_playing, dog_bark, drilling, engine_idling, gun_shot, jackhammer, siren and street_music.
The source material for the sound events are the clips from the UrbanSound8K dataset. URBAN-SED comes pre-sorted into three sets: train, validate and test. There are 6000 soundscapes in the training set, generated using clips from folds 1-6 in UrbanSound8K, 2000 soundscapes in the validation set, generated using clips from fold 7-8 in UrbanSound8K, and 2000 soundscapes in the test set, generated using clips from folds 9-10 in UrbanSound8K.

Source: [http://urbansed.weebly.com/](http://urbansed.weebly.com/)
Image Source: [http://urbansed.weebly.com/](http://urbansed.weebly.com/)
TUT-SED Synthetic 2016
Full name: TUT-SED Synthetic 2016

**TUT-SED Synthetic 2016** contains of mixture signals artificially generated from isolated sound events samples. This approach is used to get more accurate onset and offset annotations than in dataset using recordings from real acoustic environments where the annotations are always subjective.
Mixture signals in the dataset are created by randomly selecting and mixing isolated sound events from 16 sound event classes together. The resulting mixtures contains sound events with varying polyphony. All together 994 sound event samples were purchased from Sound Ideas. From the 100 mixtures created, 60% were assigned for training, 20% for testing and 20% for validation. The total amount of audio material in the dataset is 566 minutes.
Different instances of the sound events are used to synthesize the training, validation and test partitions. Mixtures were created by randomly selecting event instance and from it, randomly, a segment of length 3-15 seconds. Between events, random length silent region was introduced. Such tracks were created for four to nine event classes, and were then mixed together to form the mixture signal. As sound events are not consistently active during the samples (e.g. footsteps), automatic signal energy based annotation was applied to obtain accurate event activity within the sample. Annotation of the mixture signal was created by pooling together event activity annotation of used samples.

Source: [https://webpages.tuni.fi/arg/paper/taslp2017-crnn-sed/tut-sed-synthetic-2016](https://webpages.tuni.fi/arg/paper/taslp2017-crnn-sed/tut-sed-synthetic-2016)
Image Source: [https://arxiv.org/abs/1702.06286](https://arxiv.org/abs/1702.06286)

Introducing paper: Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection
ISMIR Genre
Full name: ISMIR2004 Genre

ISMIR2004 is an audio dataset consisting of 6 genres with 729 excerpts of 30 seconds. It is a dataset used for musical genre classification. The training set consists of 320 classical music samples, 115 electronic music samples, 26 jazz blues samples, 45 metal/punk samples, 101 rock/pop samples and 122 world samples.

Source: [http://ismir2004.ismir.net/genre_contest/index.html](http://ismir2004.ismir.net/genre_contest/index.html)
Image Source: [http://ismir2004.ismir.net/genre_contest/index.html](http://ismir2004.ismir.net/genre_contest/index.html)
NES-MDB
Full name: Nintendo Entertainment System Music Database

The **Nintendo Entertainment System Music Database** (**NES-MDB**) is a dataset intended for building automatic music composition systems for the NES audio synthesizer. It consists of  5278 songs from the soundtracks of 397 NES games. The dataset represents 296 unique composers, and the songs contain more than two million notes combined. It has file format options for MIDI, score and NLM (NES Language Modeling).

Source: [https://github.com/chrisdonahue/nesmdb](https://github.com/chrisdonahue/nesmdb)
Image Source: [https://github.com/chrisdonahue/nesmdb](https://github.com/chrisdonahue/nesmdb)
Audio Source: [https://github.com/chrisdonahue/nesmdb](https://github.com/chrisdonahue/nesmdb)

Introducing paper: The NES Music Database: A multi-instrumental dataset with expressive performance attributes
CLO-43SD
Full name: CLO-43SD

**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.

Source: [https://wp.nyu.edu/birdvox/codedata/](https://wp.nyu.edu/birdvox/codedata/)
Image Source: [https://www.allaboutbirds.org/a-rosetta-stone-for-identifying-warblers-migration-calls/](https://www.allaboutbirds.org/a-rosetta-stone-for-identifying-warblers-migration-calls/)
CLO-WTSP
Full name: CLO-WTSP

**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.​

Source: [https://wp.nyu.edu/birdvox/codedata/](https://wp.nyu.edu/birdvox/codedata/)
Image Source: [https://en.wikipedia.org/wiki/White-throated_sparrow#/media/File:Sparrow,_White_throated.jpg](https://en.wikipedia.org/wiki/White-throated_sparrow#/media/File:Sparrow,_White_throated.jpg)
CLO-SWTH
Full name: CLO-SWTH

**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.

Source: [https://wp.nyu.edu/birdvox/codedata/](https://wp.nyu.edu/birdvox/codedata/)
Image Source: [https://commons.wikimedia.org/wiki/Category:Catharus_ustulatus#/media/File:A_Swainson's_thrush_perched_in_a_tree_(7d58595b-c495-4744-9f20-2b301fa1cc63).jpg](https://commons.wikimedia.org/wiki/Category:Catharus_ustulatus#/media/File:A_Swainson's_thrush_perched_in_a_tree_(7d58595b-c495-4744-9f20-2b301fa1cc63).jpg)
Bach Doodle
Full name: Bach Doodle

The **Bach Doodle** Dataset is composed of 21.6 million harmonizations submitted from the Bach Doodle. The dataset contains both metadata about the composition (such as the country of origin and feedback), as well as a MIDI of the user-entered melody and a MIDI of the generated harmonization. The dataset contains about 6 years of user entered music.

Source: [https://magenta.tensorflow.org/datasets/bach-doodle](https://magenta.tensorflow.org/datasets/bach-doodle)
Image Source: [https://magenta.tensorflow.org/datasets/bach-doodle](https://magenta.tensorflow.org/datasets/bach-doodle)

Introducing paper: The Bach Doodle: Approachable music composition with machine learning at scale
DCASE 2018 Task 4
Full name: DCASE 2018 Task 4

DCASE2018 Task 4 is a dataset for large-scale weakly labeled semi-supervised sound event detection in domestic environments. The data are YouTube video excerpts focusing on domestic context which could be used for example in ambient assisted living applications. The domain was chosen due to the scientific challenges (wide variety of sounds, time-localized events...) and potential industrial applications.
Specifically, the task employs a subset of “Audioset: An Ontology And Human-Labeled Dataset For Audio Events” by Google. Audioset consists of an expanding ontology of 632 sound event classes and a collection of 2 million human-labeled 10-second sound clips (less than 21% are shorter than 10-seconds) drawn from 2 million Youtube videos. The ontology is specified as a hierarchical graph of event categories, covering a wide range of human and animal sounds, musical instruments and genres, and common everyday environmental sounds.
Task 4 focuses on a subset of Audioset that consists of 10 classes of sound events: speech, dog, cat, alarm bell ringing, dishes, frying, blender, running water, vacuum cleaner, electric shaver toothbrush.

Source: [http://dcase.community/challenge2018/index](http://dcase.community/challenge2018/index)
Image Source: [http://dcase.community/challenge2018/task-large-scale-weakly-labeled-semi-supervised-sound-event-detection](http://dcase.community/challenge2018/task-large-scale-weakly-labeled-semi-supervised-sound-event-detection)

Introducing paper: Large-Scale Weakly Labeled Semi-Supervised Sound Event Detection in Domestic Environments
freefield1010
Full name: freefield1010

Freefield1010 is a collection of 7,690 excerpts from field recordings around the world, gathered by the FreeSound project, and then standardised for research.

Source: [http://dcase.community/challenge2018/task-bird-audio-detection](http://dcase.community/challenge2018/task-bird-audio-detection)
Image Source: [https://arxiv.org/pdf/1309.5275.pdf](https://arxiv.org/pdf/1309.5275.pdf)
warblrb10k
Full name: warblrb10k

**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.

Source: [http://dcase.community/challenge2018/task-bird-audio-detection](http://dcase.community/challenge2018/task-bird-audio-detection)
Image Source: [https://www.warblr.co.uk/](https://www.warblr.co.uk/)
Chernobyl
Full name: Chernobyl

**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.

Source: [http://dcase.community/challenge2018/task-bird-audio-detection](http://dcase.community/challenge2018/task-bird-audio-detection)
Image Source: [https://en.wikipedia.org/wiki/Effects_of_the_Chernobyl_disaster#/media/File:Chernobyl,_Ukraine.jpg](https://en.wikipedia.org/wiki/Effects_of_the_Chernobyl_disaster#/media/File:Chernobyl,_Ukraine.jpg)
PolandNFC
Full name: PolandNFC

**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.

Source: [http://dcase.community/challenge2018/task-bird-audio-detection](http://dcase.community/challenge2018/task-bird-audio-detection)
Image Source: [http://dcase.community/challenge2018/task-bird-audio-detection](http://dcase.community/challenge2018/task-bird-audio-detection)
NIPS4Bplus
Full name: NIPS4Bplus

**NIPS4Bplus** is a richly annotated birdsong audio dataset, that is comprised of recordings containing bird vocalisations along with their active species tags plus the temporal annotations acquired for them. It consists of around 687 recordings, 87 classes, species tags, annotations. The total duration of audio is around 1 hour.

Source: [https://peerj.com/articles/cs-223.pdf](https://peerj.com/articles/cs-223.pdf)
Image Source: [https://peerj.com/articles/cs-223.pdf](https://peerj.com/articles/cs-223.pdf)

Introducing paper: NIPS4Bplus: a richly annotated birdsong audio dataset