README.md

---
dataset_info:
- config_name: large
  features:
  - name: audio
    dtype: audio
  - name: class
    dtype: string
  - name: class_id
    dtype: int64
  - name: noise_measurement
    dtype: float64
  - name: latitude
    dtype: float64
  - name: longitude
    dtype: float64
  - name: altitude
    dtype: float64
  - name: accuracy
    dtype: float64
  - name: submitter_id
    dtype: int64
  - name: region
    dtype: string
  - name: timestamp
    dtype: string
  splits:
  - name: train
    num_bytes: 2994784490.176
    num_examples: 61828
  download_size: 2988273330
  dataset_size: 2994784490.176
- config_name: small
  features:
  - name: audio
    dtype: audio
  - name: class
    dtype: string
  - name: class_id
    dtype: int64
  - name: noise_measurement
    dtype: float64
  - name: latitude
    dtype: float64
  - name: longitude
    dtype: float64
  - name: altitude
    dtype: float64
  - name: accuracy
    dtype: float64
  - name: submitter_id
    dtype: int64
  - name: region
    dtype: string
  - name: timestamp
    dtype: string
  splits:
  - name: train
    num_bytes: 48604853.0
    num_examples: 1000
  download_size: 48494763
  dataset_size: 48604853.0
configs:
- config_name: large
  data_files:
  - split: train
    path: large/train-*
- config_name: small
  data_files:
  - split: train
    path: small/train-*
task_categories:
- audio-classification
tags:
- audio
- text
size_categories:
- 10K<n<100K
---
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
```python
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")
```