audio
audioduration (s)
1.44
44.9
class
stringclasses
19 values
class_id
int64
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noise_measurement
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latitude
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longitude
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32.7
altitude
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accuracy
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submitter_id
int64
35
45
timestamp
stringlengths
24
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herbalists
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2021-05-19T12:31:30.370Z
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generator
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street-preacher
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construction-site
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car-alarm
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herbalists
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other
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school
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regilious-venue
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motorvehicle-siren
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crowd-noise
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bar-restaurant-nightclub
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animal
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school
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generator
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mobile-music
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2021-05-10T15:14:01.456Z
boda-boda-motocyle
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street-preacher
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2021-05-14T16:28:05.883Z
community-radio
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mobile-music
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animal
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community-radio
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mobile-music
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other
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construction-site
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construction-site
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crowd-noise
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car-alarm
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mobile-music
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2021-05-11T10:03:40.844Z
fabrication-workshop
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motorvehicle-siren
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2021-05-19T14:51:58.243Z
regilious-venue
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2021-05-26T16:02:30.805Z
school
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2021-05-16T04:01:08.033Z
regilious-venue
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car-alarm
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2021-05-23T11:01:43.248Z
community-radio
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2021-05-16T08:43:32.124Z
school
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generator
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2021-05-25T13:29:00.250Z
regilious-venue
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2021-05-12T21:09:56.064Z
crowd-noise
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2021-05-12T15:13:29.658Z
herbalists
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2021-05-23T10:57:02.721Z
hawker-vendor
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2021-05-17T16:50:02.265Z
animal
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2021-05-26T16:06:07.030Z
generator
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2021-05-18T16:49:20.175Z

The urban-noise dataset consists of audio samples representing urban noise environments. It is designed for tasks such as noise classification, audio tagging, or machine learning applications in sound analysis. The dataset includes two configurations, large and small, with varying sizes of data.

Example Usage

from datasets import load_dataset

# Load the large configuration
large_dataset = load_dataset("Sunbird/urban-noise", "large")

# Load the small configuration
small_dataset = load_dataset("Sunbird/urban-noise", "small")
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