README.md

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

1. Mechanical Sounds:

   • Transportation (airplane, bus, car)
   • Appliances (coffee machine, microwave)
   • Infrastructure (escalator, traffic lights)

2. Environmental Sounds:

   • Natural (birds chirping, wind through leaves)
   • Water (river, waterfall)
   • Urban ambient (traffic)

3. 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

1. Urban Planning:

   • Noise pollution monitoring
   • Traffic sound analysis
   • Public space acoustics

2. Smart Environments:

   • Home automation
   • Office environment monitoring
   • Ambient awareness systems

3. Transportation:

   • Public transport monitoring
   • Traffic analysis
   • Vehicle detection

4. 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

1. Indoor Sounds:

   • Home appliances
   • Office equipment
   • Building infrastructure

2. Outdoor Sounds:

   • Traffic and transportation
   • Natural elements
   • Urban activity