Datasets:

agkphysics
/

AudioSet

video_id stringlengths 11 11	labels listlengths 1 9	human_labels listlengths 1 9
--PJHxphWEs	[ "/m/09x0r", "/t/dd00088" ]	[ "Speech", "Gush" ]
--aE2O5G5WE	[ "/m/03fwl", "/m/04rlf", "/m/09x0r" ]	[ "Goat", "Music", "Speech" ]
--cB2ZVjpnA	[ "/m/01y3hg" ]	[ "Smoke detector, smoke alarm" ]
--aaILOrkII	[ "/m/032s66", "/m/073cg4" ]	[ "Gunshot, gunfire", "Cap gun" ]
-0DLPzsiXXE	[ "/m/04rlf", "/m/07qwdck" ]	[ "Music", "Ping" ]
--ekDLDTUXA	[ "/m/015lz1", "/m/07pws3f" ]	[ "Singing", "Bang" ]
-0DdlOuIFUI	[ "/m/0130jx", "/m/02jz0l", "/m/0838f" ]	[ "Sink (filling or washing)", "Water tap, faucet", "Water" ]
-0SdAVK79lg	[ "/m/0155w", "/m/01lyv", "/m/0342h", "/m/042v_gx", "/m/04rlf", "/m/04szw", "/m/07s0s5r", "/m/0fx80y", "/m/0gg8l" ]	[ "Blues", "Country", "Guitar", "Acoustic guitar", "Music", "Musical instrument", "Strum", "Plucked string instrument", "Bluegrass" ]
-1LrH01Ei1w	[ "/m/02p0sh1", "/m/04rlf" ]	[ "Traditional music", "Music" ]
-0O3e95y4gE	[ "/m/07r4wb8", "/t/dd00125" ]	[ "Knock", "Inside, small room" ]
-28U1_qW0sU	[ "/m/07qwdck" ]	[ "Ping" ]
-0mjrMposBM	[ "/m/04zmvq" ]	[ "Train whistle" ]
-11LhdJgBb8	[ "/m/04rlf", "/m/07qn4z3" ]	[ "Music", "Rattle" ]
-1iKLvsRBbE	[ "/m/015lz1", "/m/07pws3f" ]	[ "Singing", "Bang" ]
-2PDE7hUArE	[ "/m/02mk9", "/m/07pb8fc", "/t/dd00066" ]	[ "Engine", "Idling", "Medium engine (mid frequency)" ]
-275_wTLm-4	[ "/m/07q6cd_" ]	[ "Squeak" ]
-2hQKCE-oTI	[ "/m/07pbtc8" ]	[ "Walk, footsteps" ]
-2X03mO3T_U	[ "/m/04k94", "/m/0dv3j" ]	[ "Liquid", "Boiling" ]
-30H9V1IKps	[ "/m/07yv9", "/m/09x0r", "/m/0gvgw0" ]	[ "Vehicle", "Speech", "Air brake" ]
-3IGxVTJvgI	[ "/m/06bz3", "/m/09x0r" ]	[ "Radio", "Speech" ]
-38Qgsbh7NQ	[ "/m/013y1f", "/m/03xq_f" ]	[ "Organ", "Electronic organ" ]
-3hKkjKmIGE	[ "/m/019jd", "/m/06q74" ]	[ "Boat, Water vehicle", "Ship" ]
-3HYdaJyF4U	[ "/m/04rlf", "/m/06rqw" ]	[ "Music", "Ska" ]
-3pPrlCm6gg	[ "/m/01wy6" ]	[ "Clarinet" ]
-3IYpJfLVJk	[ "/m/03k3r", "/m/07q5rw0", "/m/0jbk" ]	[ "Horse", "Neigh, whinny", "Animal" ]
-4NLarMj4xU	[ "/m/04rlf", "/t/dd00034" ]	[ "Music", "Tender music" ]
-4xJv59_zcA	[ "/m/07pl1bw", "/m/083vt" ]	[ "Splinter", "Wood" ]
-4hZyOdm7EI	[ "/m/015lz1", "/m/015vgc" ]	[ "Singing", "Carnatic music" ]
-4RWqM0UCCY	[ "/m/012n7d", "/m/02mfyn", "/m/03j1ly", "/m/03kmc9" ]	[ "Ambulance (siren)", "Car alarm", "Emergency vehicle", "Siren" ]
-5-vmt2iKT0	[ "/m/02zsn", "/m/09x0r" ]	[ "Female speech, woman speaking", "Speech" ]
-5DdYkYOjy0	[ "/m/06h7j" ]	[ "Run" ]
-5GhUbDLYkQ	[ "/m/0130jx", "/m/02jz0l", "/m/0838f" ]	[ "Sink (filling or washing)", "Water tap, faucet", "Water" ]
-5Jlimvsuwo	[ "/m/011k_j", "/m/04rlf" ]	[ "Timpani", "Music" ]
-5S70zOSw30	[ "/m/030rvx", "/m/07pzfmf", "/m/09x0r" ]	[ "Buzzer", "Crackle", "Speech" ]
-5Kk-m7BmiE	[ "/m/07pyf11", "/m/07qmpdm" ]	[ "Flap", "Clatter" ]
-5f6hjZf9Yw	[ "/m/02w4v", "/m/04rlf" ]	[ "Folk music", "Music" ]
-60XojQWWoc	[ "/m/02rhddq", "/m/07r04", "/m/07yv9" ]	[ "Reversing beeps", "Truck", "Vehicle" ]
-6QGvxvaTkI	[ "/m/01p970", "/m/026t6", "/m/0l14md" ]	[ "Tabla", "Drum", "Percussion" ]
-5xOcMJpTUk	[ "/m/018vs", "/m/0342h", "/m/042v_gx", "/m/04rlf", "/m/04szw", "/m/09x0r", "/m/0fx80y" ]	[ "Bass guitar", "Guitar", "Acoustic guitar", "Music", "Musical instrument", "Speech", "Plucked string instrument" ]
-64xnyOswXA	[ "/m/0cdnk" ]	[ "Roaring cats (lions, tigers)" ]
-6RN8skIHU4	[ "/m/09x0r", "/m/0dv5r", "/t/dd00125" ]	[ "Speech", "Camera", "Inside, small room" ]
-693sNKI3iM	[ "/m/015lz1", "/m/01swy6", "/m/04rlf" ]	[ "Singing", "Yodeling", "Music" ]
-6krAYK2LLo	[ "/m/04rlf", "/m/06j64v", "/m/0l14gg" ]	[ "Music", "Middle Eastern music", "Soundtrack music" ]
-6gC_nlUBfA	[ "/m/02y_763", "/m/09x0r" ]	[ "Sliding door", "Speech" ]
-6pcgdLfb_A	[ "/m/04rlf", "/t/dd00035" ]	[ "Music", "Exciting music" ]
-70wVF5u-gg	[ "/m/07pn_8q" ]	[ "Chopping (food)" ]
-6x2PtSRfJU	[ "/m/01yrx", "/m/068hy", "/m/07qrkrw", "/m/09x0r" ]	[ "Cat", "Domestic animals, pets", "Meow", "Speech" ]
-70MzkgSsHQ	[ "/m/04rlf", "/m/04rzd" ]	[ "Music", "Mandolin" ]
-7J4109yM7w	[ "/m/02mk9", "/m/07yv9", "/t/dd00130" ]	[ "Engine", "Vehicle", "Engine starting" ]
-76P3VHPuus	[ "/m/01jg1z" ]	[ "Heart murmur" ]
-75IwDlkzDQ	[ "/m/01h82_", "/m/02mk9" ]	[ "Engine knocking", "Engine" ]
-7QVE8o9uk4	[ "/m/01yg9g", "/m/04rlf", "/m/07yv9" ]	[ "Lawn mower", "Music", "Vehicle" ]
-7ye24UFRng	[ "/m/032n05", "/m/04rlf" ]	[ "Whale vocalization", "Music" ]
-7z662AsuTE	[ "/m/0mbct" ]	[ "Gong" ]
-7wUQP6G5EQ	[ "/m/02cjck", "/m/02w4v", "/m/04rlf" ]	[ "Theme music", "Folk music", "Music" ]
-8Ezw9946g8	[ "/m/0fqfqc" ]	[ "Drawer open or close" ]
-8S_tLKfeJg	[ "/m/04fgwm", "/m/09x0r", "/t/dd00125" ]	[ "Electric toothbrush", "Speech", "Inside, small room" ]
-8CrCItCO24	[ "/m/07q7njn" ]	[ "Chink, clink" ]
-8_HpHg6nCw	[ "/m/07ptzwd", "/m/0838f" ]	[ "Pump (liquid)", "Water" ]
-8H4GKg-mYQ	[ "/m/04rlf", "/m/07gxw" ]	[ "Music", "Techno" ]
-8n2NqDFRko	[ "/m/02rhddq", "/m/07r04", "/m/07yv9", "/m/09x0r" ]	[ "Reversing beeps", "Truck", "Vehicle", "Speech" ]
-8fQePz4FcE	[ "/m/04rlf", "/m/07xzm", "/t/dd00126" ]	[ "Music", "Ukulele", "Inside, large room or hall" ]
-99daJhXYJY	[ "/m/019jd", "/m/02rlv9", "/m/03m9d0z", "/t/dd00092" ]	[ "Boat, Water vehicle", "Motorboat, speedboat", "Wind", "Wind noise (microphone)" ]
-9Nl3LZBCOo	[ "/m/01s0vc", "/m/09x0r" ]	[ "Zipper (clothing)", "Speech" ]
-92cYmTgIjM	[ "/m/02fxyj" ]	[ "Humming" ]
-9ME9qMgkeY	[ "/m/08j51y", "/m/09ct_" ]	[ "Dental drill, dentist's drill", "Helicopter" ]
-9c1c8G_WO8	[ "/m/07c52", "/m/09x0r" ]	[ "Television", "Speech" ]
-9ek6eO0RtI	[ "/m/03wwcy" ]	[ "Doorbell" ]
-9wxcZf43a4	[ "/m/0cj0r" ]	[ "Pink noise" ]
-ASYwidRD7M	[ "/m/01d3sd" ]	[ "Snoring" ]
-A0MTi2NMGI	[ "/t/dd00134" ]	[ "Car passing by" ]
-Ar7D-GARhI	[ "/m/07bgp", "/m/07q0h5t", "/m/09x0r" ]	[ "Sheep", "Bleat", "Speech" ]
-AIrHVeCgtM	[ "/m/02rhddq", "/m/07r04", "/m/07yv9", "/m/09x0r" ]	[ "Reversing beeps", "Truck", "Vehicle", "Speech" ]
-AswFDuOptQ	[ "/m/04k94", "/m/04rlf", "/m/09x0r" ]	[ "Liquid", "Music", "Speech" ]
-BIMKnb3tlo	[ "/m/01p970", "/m/026t6", "/m/0l14md" ]	[ "Tabla", "Drum", "Percussion" ]
-BqpAyCLh7U	[ "/m/01m2v", "/m/04rlf", "/m/09x0r" ]	[ "Computer keyboard", "Music", "Speech" ]
-B3_mFE3pog	[ "/m/07qfgpx" ]	[ "Jingle, tinkle" ]
-BxQ9nkrId4	[ "/m/07qv_x_" ]	[ "Shuffle" ]
-By_0iIghuE	[ "/m/01yrx", "/m/068hy", "/m/07r81j2", "/m/0jbk" ]	[ "Cat", "Domestic animals, pets", "Caterwaul", "Animal" ]
-BwM5Va6XFg	[ "/m/023pjk", "/m/04brg2" ]	[ "Cutlery, silverware", "Dishes, pots, and pans" ]
-CUp_Tmg2Y0	[ "/m/01qbl", "/m/026t6", "/m/02hnl", "/m/03qtq", "/m/03t3fj", "/m/0bm02", "/m/0l14md" ]	[ "Cymbal", "Drum", "Drum kit", "Hi-hat", "Rimshot", "Bass drum", "Percussion" ]
-C5dEU4zpJg	[ "/m/025_jnm", "/m/04rlf" ]	[ "Finger snapping", "Music" ]
-CIxoobEubQ	[ "/m/02p01q", "/m/07pdjhy", "/m/083vt" ]	[ "Filing (rasp)", "Rub", "Wood" ]
-CHMeZVAe2s	[ "/m/01jg1z" ]	[ "Heart murmur" ]
-DNkAalo7og	[ "/m/02mk9", "/m/07pb8fc", "/t/dd00066" ]	[ "Engine", "Idling", "Medium engine (mid frequency)" ]
-CniGkDLq-Y	[ "/m/04rlf", "/t/dd00004" ]	[ "Music", "Female singing" ]
-DBcp6YGi6k	[ "/t/dd00018" ]	[ "Oink" ]
-CgdUiJaNdU	[ "/m/07sq110", "/m/09x0r" ]	[ "Belly laugh", "Speech" ]
-DWRAxaH_RY	[ "/m/04rlf", "/m/09x0r", "/m/0l156b" ]	[ "Music", "Speech", "Steelpan" ]
-DLL8LaKq-c	[ "/m/07pws3f", "/m/09x0r" ]	[ "Bang", "Speech" ]
-DZMYxyB6L8	[ "/m/05lls" ]	[ "Opera" ]
-DcMk015qSA	[ "/m/07qh7jl" ]	[ "Creak" ]
-D_ljLTbCK0	[ "/m/04rlf", "/m/09ct_" ]	[ "Music", "Helicopter" ]
-DeAdhYKbGE	[ "/m/0192l", "/m/085jw" ]	[ "Bagpipes", "Wind instrument, woodwind instrument" ]
-Dtir74TiUM	[ "/m/01hgjl", "/m/0283d", "/m/04rlf", "/m/07qb_dv" ]	[ "Scratching (performance technique)", "Drum and bass", "Music", "Scratch" ]
-Dj2PfPmynQ	[ "/m/04rlf", "/m/0l14jd", "/t/dd00003", "/t/dd00004" ]	[ "Music", "Choir", "Male singing", "Female singing" ]
-Dl-VzBPd9E	[ "/m/028ght" ]	[ "Applause" ]
-DvxsHG1tuo	[ "/m/04rlf", "/m/05zppz", "/m/07sr1lc", "/m/09x0r" ]	[ "Music", "Male speech, man speaking", "Yell", "Speech" ]
-EDGR1davAc	[ "/m/0gy1t2s" ]	[ "Bicycle bell" ]
-ECK_BisOLM	[ "/m/04rlf", "/t/dd00006" ]	[ "Music", "Synthetic singing" ]

End of preview. Expand in Data Studio

Dataset Card for AudioSet

Dataset Summary

AudioSet is a dataset of 10-second clips from YouTube, annotated into one or more sound categories, following the AudioSet ontology.

Supported Tasks and Leaderboards

audio-classification: Classify audio clips into categories. The leaderboard is available here

Languages

The class labels in the dataset are in English.

Dataset Structure

Data Instances

Example instance from the dataset:

{
 'video_id': '--PJHxphWEs',
 'audio': {
  'path': 'audio/bal_train/--PJHxphWEs.flac',
  'array': array([-0.04364824, -0.05268681, -0.0568949 , ...,  0.11446512,
          0.14912748,  0.13409865]),
  'sampling_rate': 48000
 },
 'labels': ['/m/09x0r', '/t/dd00088'],
 'human_labels': ['Speech', 'Gush']
}

Data Fields

Instances have the following fields:

video_id: a string feature containing the original YouTube ID.
audio: an Audio feature containing the audio data and sample rate.
labels: a sequence of string features containing the labels associated with the audio clip.
human_labels: a sequence of string features containing the human-readable forms of the same labels as in labels.

Data Splits

The distribuion of audio clips is as follows:

`balanced` configuration

	train	test
# instances	18683	17141

`unbalanced` configuration

	train	test
# instances	1738657	17141

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

The labels are from the AudioSet ontology. Audio clips are from YouTube.

Annotations

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

The YouTube videos in this copy of AudioSet were downloaded in March 2023, so not all of the original audios are available. The number of clips able to be downloaded is as follows:
- Balanced train: 18683 audio clips out of 22160 originally.
- Unbalanced train: 1738788 clips out of 2041789 originally.
- Evaluation: 17141 audio clips out of 20371 originally.
Most audio is sampled at 48 kHz 24 bit, but about 10% is sampled at 44.1 kHz 24 bit. Audio files are stored in the FLAC format.

Additional Information

Dataset Curators

More Information Needed

Licensing Information

The AudioSet data is licensed under CC-BY-4.0

Citation

@inproceedings{jort_audioset_2017,
    title	= {Audio Set: An ontology and human-labeled dataset for audio events},
    author	= {Jort F. Gemmeke and Daniel P. W. Ellis and Dylan Freedman and Aren Jansen and Wade Lawrence and R. Channing Moore and Manoj Plakal and Marvin Ritter},
    year	= {2017},
    booktitle	= {Proc. IEEE ICASSP 2017},
    address	= {New Orleans, LA}
}