Datasets:

Titung
/

MIVIA

license: other
task_categories:
  - audio-classification
tags:
  - audio
  - road-sounds
  - sound-event-detection
  - environmental-sound
size_categories:
  - n<1K

MIVIA Road Events Dataset

Dataset Summary

MIVIA_ROAD_DB1 is an annotated audio dataset for road acoustic event detection. 57 scene recordings with precise timestamped annotations for two classes of road sound events. Originally released by the MIVIA Lab, University of Salerno.

Dataset Viewer

The viewer above shows one scene audio file per row. Use MIVIA_events.csv for the full per-event annotation table (400 events total).

Statistics


Scene recordings	57
Total events	400
Class 2 events	200
Class 3 events	200
Audio format	WAV, 32000 Hz, mono
Avg duration	~65–70 seconds

Files

File	Description
`data/*.wav`	57 scene audio recordings
`metadata.csv`	Scene-level metadata (powers the viewer)
`MIVIA_events.csv`	Full event table (400 rows, timestamps per event)
`MIVIA_events.json`	Same data in JSON format

Columns in MIVIA_events.csv

Column	Description
`xml_file`	Source annotation XML
`scene_audio`	Mixed scene WAV file
`event_wav`	Isolated event sound file
`class_id`	2 or 3
`class_name`	Event class name
`start_sec`	Event start time (seconds)
`end_sec`	Event end time (seconds)
`bg_class_name`	Background sound class
`bg_pathname`	Background audio file

Usage

import pandas as pd
from huggingface_hub import hf_hub_download

# Load full event annotations
csv_path = hf_hub_download(
    repo_id="Titung/MIVIA",
    filename="MIVIA_events.csv",
    repo_type="dataset"
)
df = pd.read_csv(csv_path)
print(df.head())
print(df["class_id"].value_counts())

# Load a scene audio file
from huggingface_hub import hf_hub_download
import librosa

wav_path = hf_hub_download(
    repo_id="Titung/MIVIA",
    filename="data/00001_1.wav",
    repo_type="dataset"
)
y, sr = librosa.load(wav_path, sr=None)

# Slice first event
row = df.iloc[0]
event_audio = y[int(row.start_sec * sr) : int(row.end_sec * sr)]
print(f"Event duration: {len(event_audio)/sr:.2f}s  Class: {row.class_id}")

Citation

Foggia, P., Petkov, N., Saggese, A., Strisciuglio, N., & Vento, M. (2015). Reliable detection of audio events in highly noisy environments. Pattern Recognition Letters. Elsevier.

MIVIA Lab, University of Salerno — http://mivia.unisa.it