MAVD-traffic dataset

sounds

General information

Label Value Description
Name MAVD-traffic dataset Full dataset name
ID sounds/mavd Datalist id for external indexing
Abbreviation MAVD Official dataset abbreviation, e.g. one used in the original paper
Provider MAVD
Year 2019 Dataset release year
Modalities Audio Video Data modalities included in the dataset
Collection name MAVD Common name for all related datasets, used to group datasets coming from same source
Research domain SED Strong annotation Traffic Audio-visual Related domains, e.g., Scenes, Mobile devices, Audio-visual, Open set, Ambient noise, Unlabelled, Multiple sensors, SED, SELD, Tagging, FL, Strong annotation, Weak annotation, Unlabelled, Multi-annotator
License Creative Commons, CC BY 4.0
Download Download (1.3GB)
Citation [Zinemanas2019] MAVD: A Dataset for Sound Event Detection in Urban Environments

Audio

Label Value Description

Data

Data type Audio Possible values: Audio | Features
File format
File format type Constant Possible values: Constant | Variable
File format flac Possible value: wav | aiff | flac | mp3 | aac | ogg
Lossy compression No is audio compressed in a lossy manner
Bit rate 24 Bit depth of audio, possible values: 8 | 16 | 24 | 32
Sampling rate (kHz) 44.1 kHz Sampling rate in kHz, possible values: 8 | 16 | 22.05 | 32 | 44.1 | 48
Channels
Setup Mono Possible values: Mono | Stereo | Binaural | Ambisonic | Array | Multi-Channel | Variable
Number of channels 1
Material
Source Original Possible values: Original | Youtube | Freesound | Online | Crowdsourced | [Dataset name]

Content

Content type Freefield Possible values: Freefield | Synthetic | Isolated
Scene Constant Is the scene class constant for single recording, possible values: Constant | Variable

Recording

Setup Uncontrolled Possible values: Near-field | Far-field | Mixed | Uncontrolled | Unknown
Setup count 1 Amount of different recording setups (microphone and recording device) used
Spot type Fixed Possible values: Fixed | Moving | Unknown

Files

Count 47 files Total number of files
Total duration (minutes) 235 min Total duration of the dataset in minutes
File length Constant Characterization of the file lengths, possible values: Constant | Quasi-constant | Variable
File length (seconds) 300 sec Approximate length of files

Meta

Label Value Description
Types Event List of meta data types provided for the data, possible values: Event, Tag, Scene, Caption, Geolocation, Spatial location, Annotator, Timestamp, Presence, Proximity, etc.

Scene

Classes 1 Number of scene classes
Classes True Possible values: True | False | Almost
Classes
  • Traffic

Event

Classes 21 Number of event classes
Classes False Possible values: True | False | Almost
Classes
  • bus
  • bus/brakes
  • bus/compressor
  • bus/engine_accelerating
  • bus/engine_idling
  • bus/wheel_rolling
  • car
  • car/engine_accelerating
  • car/engine_idling
  • car/wheel_rolling
  • chatter
  • motorcycle
  • motorcycle/brakes
  • motorcycle/engine_accelerating
  • motorcycle/engine_idling
  • music
  • other
  • truck
  • truck/brakes
  • truck/compressor
  • truck/engine_accelerating
  • truck/wheel_rolling
Annotation
Type Strong Possible values: Strong | Weak | Location | None
Source Experts Possible values: Experts | Crowdsourced | Synthetic | Metadata | Automatic
Annotations per item 1 How many annotations there are available per item (possible multi-annotator setup)
Labelled amount (%) 100 % Percentage of all data, amount of data which is labelled
Strong annotations amount (%) 100 % Percentage of all data, amount of data which has strong annotations
Overlapping event instances Yes
Labeling
Hierarchical Yes
Ontology name Yes
Instance
Count 3427 Count of all event instances in the dataset
Average instances per class 163.19047619 Average per class instance count

Cross-validation setup

Label Value Description
Provided Yes
Folds 1
Sets Train Val Test Set types provided in the split, possible values: Train | Test | Val | Dev | Eval

Baseline

Label Value Description
Download Download Link to baseline system source code
Citation [Zinemanas2019] Paper to cite for the baseline