Titung/MIVIA-GENERAL

MIVIA DB4 – Audio Surveillance Event Dataset

⚠️ Private repository — This dataset is derived from the MIVIA DB4 research dataset (University of Salerno). Not for public redistribution.

Dataset Summary

MIVIA DB4 is a benchmark dataset for acoustic surveillance event detection in urban environments. It contains annotated scene recordings with three target event classes embedded in realistic background noise.

Original dataset: MIVIA Lab, University of Salerno
Reference: Foggia et al., IEEE TIFS 2016

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Statistics

	Train	Test	Total
Scenes	66	29	95
Events	2,100	900	3,000
Glass events	700	300	1,000
Gunshot events	700	300	1,000
Scream events	700	300	1,000
Audio format	32000 Hz mono	32000 Hz mono	—
Scene duration	~180 s	~180 s	—

Perfectly balanced — 1,000 events per class, 70/30 train/test split.

Event Classes

CLASS_ID	Category	Description
1	background	Ambient background (not a target event)
2	glass	Glass breaking sounds
3	gunshots	Gunshot sounds
4	screams	Human screams

Background Subclasses

bells, cars, crowd, crowd_claps, gaussian_noise, household_app, rain, twistle

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Files

File	Description
data/train/*.wav	528 training scene recordings
data/test/*.wav	232 testing scene recordings
DB4_train_events.csv	2,100 training event annotations
DB4_test_events.csv	900 testing event annotations
DB4_all_events.csv	All 3,000 events combined
metadata.csv	Scene metadata (powers Dataset Viewer)

CSV Columns

Column	Example	Description
xml_file	00001.xml	Source annotation file
split	train	train or test
scene_base	00001	Scene ID
event_pathname	glass/0001.wav	Isolated event sound
event_category	glass	glass / gunshots / screams
class_id	2	Numeric class label
start_sec	5.3647	Event start time (s)
end_sec	5.8497	Event end time (s)
bg_subclass	cars	Background type
bg_pathname	background/cars/0026.wav	Background file
audio_exists	true	WAV found on disk

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Usage in Colab

import pandas as pd
from huggingface_hub import hf_hub_download

# Load training annotations
train_path = hf_hub_download(
    repo_id="Titung/MIVIA-GENERAL",
    filename="DB4_train_events.csv",
    repo_type="dataset"
)
train_df = pd.read_csv(train_path)
print(train_df["event_category"].value_counts())

# Load a scene audio file and slice an event
import librosa
from huggingface_hub import hf_hub_download

wav_path = hf_hub_download(
    repo_id="Titung/MIVIA-GENERAL",
    filename="data/train/00001_00.wav",
    repo_type="dataset"
)
y, sr = librosa.load(wav_path, sr=None)

row = train_df.iloc[0]
event = y[int(row.start_sec * sr) : int(row.end_sec * sr)]
print(f"Class: {row.event_category} | Duration: {len(event)/sr:.3f}s")

# Load with HF datasets library
from datasets import load_dataset

ds = load_dataset(
    "Titung/MIVIA-GENERAL",
    data_files={"train": "DB4_train_events.csv",
                 "test":  "DB4_test_events.csv"}
)
print(ds)

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Citation

Foggia, P., Petkov, N., Saggese, A., Strisciuglio, N., & Vento, M. (2016).
Recognizing and Localizing the Sounds of Abnormal Events in Urban Environments.
IEEE Transactions on Information Forensics and Security, 11(5), 1026–1037.

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