audio
audioduration (s) 0.51
10
| sound
sequencelengths 1
12
| label
sequencelengths 1
12
|
|---|---|---|
[
"Jingle bell"
] | [
470
] |
|
[
"Applause"
] | [
9
] |
|
[
"Pour",
"Speech"
] | [
328,
208
] |
|
[
"Whistle"
] | [
386
] |
|
[
"Music",
"Roll",
"Speech"
] | [
18,
42,
208
] |
|
[
"Fire"
] | [
513
] |
|
[
"Music",
"Bellow",
"Whoop"
] | [
18,
322,
152
] |
|
[
"Fire",
"Wind",
"Wind noise (microphone)"
] | [
513,
520,
494
] |
|
[
"Busy signal"
] | [
464
] |
|
[
"Dial tone",
"Music"
] | [
164,
18
] |
|
[
"Ping"
] | [
510
] |
|
[
"Music",
"Ska"
] | [
18,
458
] |
|
[
"Turkey",
"Fowl",
"Gobble"
] | [
35,
130,
242
] |
|
[
"Squawk"
] | [
134
] |
|
[
"Chainsaw",
"Speech",
"Inside, small room"
] | [
201,
208,
273
] |
|
[
"Scissors",
"Inside, small room"
] | [
296,
273
] |
|
[
"Sampler",
"Music",
"Speech"
] | [
158,
18,
208
] |
|
[
"Heart sounds, heartbeat"
] | [
286
] |
|
[
"Electronic music",
"Music",
"Opera",
"Techno"
] | [
45,
18,
247,
477
] |
|
[
"Engine knocking",
"Engine"
] | [
521,
496
] |
|
[
"Music",
"Techno"
] | [
18,
477
] |
|
[
"Boat, Water vehicle",
"Wind",
"Ship",
"Wind noise (microphone)"
] | [
176,
520,
292,
494
] |
|
[
"Cupboard open or close"
] | [
366
] |
|
[
"Domestic animals, pets",
"Duck",
"Speech"
] | [
514,
447,
208
] |
|
[
"Music",
"Roll",
"Rock and roll"
] | [
18,
42,
403
] |
|
[
"Harpsichord",
"Keyboard (musical)"
] | [
308,
3
] |
|
[
"Sine wave"
] | [
28
] |
|
[
"Guitar",
"Acoustic guitar",
"Music",
"Musical instrument",
"Strum",
"Speech",
"Plucked string instrument"
] | [
284,
259,
18,
436,
302,
208,
245
] |
|
[
"Grunt",
"Sound effect"
] | [
234,
210
] |
|
[
"Speech",
"Crumpling, crinkling",
"Inside, small room"
] | [
208,
475,
273
] |
|
[
"Chopping (food)"
] | [
341
] |
|
[
"Music",
"Tender music"
] | [
18,
319
] |
|
[
"Scratching (performance technique)",
"Music",
"Musical instrument"
] | [
85,
18,
436
] |
|
[
"Burping, eructation",
"Speech"
] | [
493,
208
] |
|
[
"Sidetone"
] | [
423
] |
|
[
"Fire"
] | [
513
] |
|
[
"Soul music"
] | [
331
] |
|
[
"Boat, Water vehicle",
"Motorboat, speedboat"
] | [
176,
483
] |
|
[
"Plop",
"Speech"
] | [
193,
208
] |
|
[
"Music",
"Zing"
] | [
18,
166
] |
|
[
"Music",
"Speech",
"Electronic tuner"
] | [
18,
208,
397
] |
|
[
"Theme music",
"Music"
] | [
448,
18
] |
|
[
"Door",
"Sliding door",
"Speech"
] | [
382,
352,
208
] |
|
[
"Boat, Water vehicle",
"Waves, surf",
"Wind",
"Ocean",
"Sailboat, sailing ship",
"Wind noise (microphone)"
] | [
176,
244,
520,
329,
301,
494
] |
|
[
"Conversation",
"Goat",
"Speech",
"Livestock, farm animals, working animals",
"Animal"
] | [
52,
189,
208,
139,
404
] |
|
[
"Rodents, rats, mice"
] | [
253
] |
|
[
"Guitar",
"Music",
"Musical instrument",
"Theremin",
"Speech",
"Plucked string instrument",
"Inside, small room"
] | [
284,
18,
436,
133,
208,
245,
273
] |
|
[
"Music",
"Speech",
"Tender music"
] | [
18,
208,
319
] |
|
[
"Speech",
"Breathing"
] | [
208,
235
] |
|
[
"Gunshot, gunfire",
"Artillery fire"
] | [
57,
73
] |
|
[
"Tuning fork"
] | [
424
] |
|
[
"Plop",
"Speech"
] | [
193,
208
] |
|
[
"Stream"
] | [
427
] |
|
[
"Music",
"Speech",
"Throat clearing"
] | [
18,
208,
336
] |
|
[
"Music",
"Single-lens reflex camera"
] | [
18,
129
] |
|
[
"Guitar",
"Music",
"Musical instrument",
"Ukulele",
"Speech",
"Plucked string instrument"
] | [
284,
18,
436,
375,
208,
245
] |
|
[
"Male speech, man speaking",
"Speech"
] | [
2,
208
] |
|
[
"Tearing",
"Speech"
] | [
124,
208
] |
|
[
"Traffic noise, roadway noise"
] | [
19
] |
|
[
"Gunshot, gunfire",
"Machine gun",
"Sound effect"
] | [
57,
214,
210
] |
|
[
"Cacophony"
] | [
108
] |
|
[
"Throbbing"
] | [
224
] |
|
[
"Chopping (food)"
] | [
341
] |
|
[
"Shuffle",
"Speech"
] | [
330,
208
] |
|
[
"Music",
"Walk, footsteps"
] | [
18,
126
] |
|
[
"Explosion",
"Hiccup",
"Music",
"Burst, pop",
"Speech"
] | [
417,
305,
18,
211,
208
] |
|
[
"Music",
"Spray",
"Speech",
"Inside, public space"
] | [
18,
230,
208,
99
] |
|
[
"Beatboxing"
] | [
83
] |
|
[
"Crackle",
"Speech"
] | [
94,
208
] |
|
[
"Singing",
"Music",
"Shout"
] | [
377,
18,
109
] |
|
[
"Arrow"
] | [
163
] |
|
[
"Power windows, electric windows",
"Vehicle",
"Speech",
"Car"
] | [
0,
226,
208,
107
] |
|
[
"Snoring",
"Domestic animals, pets",
"Dog",
"Animal"
] | [
190,
514,
246,
404
] |
|
[
"Hammer"
] | [
317
] |
|
[
"Sneeze",
"Sniff"
] | [
184,
289
] |
|
[
"Singing",
"Yodeling",
"Music"
] | [
377,
491,
18
] |
|
[
"Rustling leaves",
"Speech"
] | [
160,
208
] |
|
[
"Effects unit",
"Guitar",
"Music",
"Musical instrument",
"Chorus effect",
"Plucked string instrument"
] | [
368,
284,
18,
436,
40,
245
] |
|
[
"Fowl",
"Bird flight, flapping wings",
"Cluck",
"Chicken, rooster"
] | [
130,
340,
268,
11
] |
|
[
"Afrobeat",
"Disco",
"Music"
] | [
275,
490,
18
] |
|
[
"Toot",
"Speech"
] | [
335,
208
] |
|
[
"Yodeling",
"Music"
] | [
491,
18
] |
|
[
"Music",
"Ding-dong"
] | [
18,
69
] |
|
[
"Electric guitar"
] | [
316
] |
|
[
"Electric shaver, electric razor",
"Inside, small room"
] | [
169,
273
] |
|
[
"Fusillade",
"Music",
"Speech"
] | [
345,
18,
208
] |
|
[
"Brass instrument",
"French horn"
] | [
115,
346
] |
|
[
"Heavy metal",
"Music",
"Punk rock",
"Progressive rock"
] | [
98,
18,
56,
191
] |
|
[
"Female speech, woman speaking",
"Speech"
] | [
267,
208
] |
|
[
"Brass instrument",
"Saxophone"
] | [
115,
203
] |
|
[
"Singing",
"Music",
"Rock music",
"Roll",
"Rock and roll"
] | [
377,
18,
120,
42,
403
] |
|
[
"Vibraphone"
] | [
395
] |
|
[
"Banjo",
"Country"
] | [
414,
159
] |
|
[
"Creak",
"Mechanisms"
] | [
144,
48
] |
|
[
"Keys jangling",
"Vehicle",
"Speech",
"Car"
] | [
229,
226,
208,
107
] |
|
[
"Chink, clink"
] | [
151
] |
|
[
"Gunshot, gunfire",
"Cap gun"
] | [
57,
209
] |
|
[
"Bagpipes",
"Wind instrument, woodwind instrument"
] | [
435,
282
] |
|
[
"Narration, monologue",
"Music",
"Speech"
] | [
334,
18,
208
] |
|
[
"Music",
"Domestic animals, pets",
"Speech",
"Dog",
"Growling",
"Animal",
"Inside, small room"
] | [
18,
514,
208,
246,
265,
404,
273
] |
End of preview. Expand
in Dataset Viewer.
AudioSet
The AudioSet dataset is a large-scale collection of human-labelled 10-second sound clips drawn from YouTube videos. We download the AudioSet database from here. This downloaded version contains 20550 / 22160 of the balaned training subset, and 18887 / 20371 of the evaluation subset. So far, we only provide the balanced version training subset, the unbalanced version training subset will be uploaded in the near future.
Citation
@INPROCEEDINGS{7952261,
author={Gemmeke, Jort F. and Ellis, Daniel P. W. and Freedman, Dylan and Jansen, Aren and Lawrence, Wade and Moore, R. Channing and Plakal, Manoj and Ritter, Marvin},
booktitle={2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
title={Audio Set: An ontology and human-labeled dataset for audio events},
year={2017},
pages={776-780},
keywords={Ontologies;Birds;Music;Taxonomy;Labeling;Audio event detection;sound ontology;audio databases;data collection},
doi={10.1109/ICASSP.2017.7952261}
}
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