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ambient_sounds

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train · 39 rows

audioFile stringlengths 7 25	primarySound stringlengths 3 21	soundSource stringclasses 6 values	environment stringclasses 5 values	timeOfDay stringclasses 2 values	perceivedLoudness stringclasses 3 values	soundOverlap stringclasses 6 values	weather stringclasses 1 value
airplane_overhead.m4a	airplane overhead	vehicle	outdoor_urban	morning	quiet	none	sunny
airplane_overhead_2.m4a	airplane overhead	mechanical	Outdoor_urban	morning	Moderate	wind, traffic	sunny
background_music.m4a	background_music	mechanical	Outdoor_urban	morning	quiet	Traffic	sunny
background_music_2.m4a	background_music	mechanical	indoor_office	morning	quiet	wind	sunny
bike_bell.m4a	Bike_bell	mechanical	Outdoor_urban	morning	Moderate	Traffic	sunny
birds_chirping.m4a	Birds_chirping	Enviornmental	Outdoor_urban	morning	Moderate	none	sunny
birds_chirping_2.m4a	Birds_chirping	Enviornmental	Outdoor_urban	morning	Moderate	none	sunny
bus.m4a	Bus	mechanical	Outdoor_urban	morning	loud	Traffic	sunny
car_engine.m4a	Car_engine	mechanical	Outdoor_urban	morning	loud	Traffic	sunny
car_reverse.m4a	Car_reverse	mechanical	Outdoor_urban	morning	loud	none	sunny
coffee_machine.m4a	Coffee_machine	mechanical	indoor_home	afternoon	loud	none	sunny
coughing.m4a	Coughing	Human	indoor_office	morning	loud	none	sunny
cutlery.m4a	Cutlery	mechanical	indoor_home	afternoon	loud	none	sunny
door_closing.m4a	Door_closing	mechanical	indoor_office	morning	Moderate	none	sunny
door_closing_2.m4a	Door_closing	mechanical	indoor_office	morning	loud	none	sunny
doorbell.m4a	Doorbell	mechanical	indoor_home	afternoon	loud	none	sunny
escalator.m4a	Escalator	mechanical	public_transport	morning	loud	Traffic	sunny
footsteps.m4a	Footsteps	Human	indoor_office	morning	quiet	none	sunny
footsteps_on_concrete.m4a	Footsteps_on_concrete	Human	Outdoor_urban	morning	Moderate	Traffic	sunny
fridge_humming.m4a	Fridge_humming	mechanical	indoor_home	afternoon	quiet	none	sunny
hair_dryer.m4a	Hair_dryer	mechanical	indoor_home	afternoon	loud	none	sunny
honking.m4a	Honking	mechanical	Outdoor_urban	morning	loud	Traffic	sunny
knock.m4a	Knock	Human	indoor_home	afternoon	loud	none	sunny
leafes.m4a	Leafes	Enviornmental	Outdoor_urban	morning	quiet	people_talking, Wind	sunny
microwave.m4a	Microwave	mechanical	indoor_home	afternoon	loud	none	sunny
motorcycle_engine.m4a	Motorcycle_engine	mechanical	Outdoor_urban	morning	loud	Traffic	sunny
music_background.m4a	music_background	music_device	outdoor_urban	afternoon	quiet	none	sunny
river.m4a	River	Enviornmental	Outdoor_urban	morning	loud	none	sunny
shower.m4a	Shower	Mechanical	indoor_home	afternoon	loud	none	sunny
subway_arriving.m4a	Subway_arriving	Mechanical	public_transport	morning	loud	people_talking	sunny
subway_passing.m4a	Subway_passing	Mechanical	public_transport	morning	loud	people_talking	sunny
toilette_flush.m4a	Toilette_flush	Mechanical	Outdoor_urban	afternoon	loud	none	sunny
traffic.m4a	Traffic	Mechanical	Outdoor_urban	morning	quiet	Traffic	sunny
traffic_light.m4a	Traffic_light	Mechanical	outdoor_urban	morning	quiet	Traffic	sunny
typing_on_keyboard.m4a	Typing_on_keyboard	mechanical	indoor_office	morning	loud	none	sunny
vacumme_cleaner.m4a	Vacumme_cleaner	Enviornmental	indoor_home	afternoon	loud	none	sunny
water.m4a	Water	Mechanical	indoor_home	afternoon	loud	none	sunny
waterfall.m4a	Waterfall	Enviornmental	outdoor_urban	afternoon	loud	none	sunny
wind_through_leaves.m4a	Wind_through_leaves	Enviornmental	outdoor_urban	afternoon	quiet	none	sunny

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Check out the documentation for more information.

Ambient Sounds Dataset

A diverse collection of environmental and mechanical sounds recorded in various urban and indoor settings. The dataset captures everyday ambient sounds with detailed contextual metadata including environment type, time of day, and sound characteristics.

Dataset Description

This dataset contains 39 high-quality audio recordings of ambient sounds commonly encountered in urban and indoor environments. Each recording is carefully categorized and annotated with environmental context, sound source information, and acoustic characteristics.

Key Features

Total Samples: 39 recordings
Audio Format: M4A
Environments: 5 types
Time Periods: 2 (morning, afternoon)
Sound Sources: 6 categories
Weather Condition: Sunny

Sound Categories

Mechanical Sounds:
- Transportation (airplane, bus, car)
- Appliances (coffee machine, microwave)
- Infrastructure (escalator, traffic lights)
Environmental Sounds:
- Natural (birds chirping, wind through leaves)
- Water (river, waterfall)
- Urban ambient (traffic)
Human-Generated:
- Movement (footsteps)
- Interactions (knock, typing)
- Body sounds (coughing)

Environmental Types

Indoor Home
Indoor Office
Outdoor Urban
Public Transport
Mixed Environments

Data Format

Each entry contains:

{
  "audioFile": "string",
  "primarySound": "string",
  "soundSource": "string",
  "environment": "string",
  "timeOfDay": "string",
  "perceivedLoudness": "string",
  "soundOverlap": "string",
  "weather": "string"
}

Sound Characteristics

Loudness Levels

Quiet
Moderate
Loud

Sound Overlap Types

None
Traffic
Wind
People talking
Mixed

Usage

This dataset is particularly useful for:

Environmental sound classification
Urban noise monitoring
Smart home applications
Acoustic scene analysis
Background noise modeling
Sound event detection

Loading the Dataset

from datasets import load_dataset

dataset = load_dataset("maxF6YsK/ambient_sounds")

Dataset Statistics

Distribution by Environment

Outdoor Urban: ~40%
Indoor Home: ~25%
Indoor Office: ~20%
Public Transport: ~15%

Distribution by Sound Source

Mechanical: ~50%
Environmental: ~25%
Human: ~15%
Vehicle: ~10%

Time Distribution

Morning: ~60%
Afternoon: ~40%

Applications

Urban Planning:
- Noise pollution monitoring
- Traffic sound analysis
- Public space acoustics
Smart Environments:
- Home automation
- Office environment monitoring
- Ambient awareness systems
Transportation:
- Public transport monitoring
- Traffic analysis
- Vehicle detection
Environmental Monitoring:
- Urban soundscape analysis
- Natural sound detection
- Weather impact studies

Technical Details

Recording Conditions

Weather: Sunny
Consistent recording quality
Various acoustic environments
Multiple overlap scenarios

Sound Categories

Indoor Sounds:
- Home appliances
- Office equipment
- Building infrastructure
Outdoor Sounds:
- Traffic and transportation
- Natural elements
- Urban activity

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