Datasets:
metadata
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
num_examples: 1000
download_size: 48494763
dataset_size: 48604853
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
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")