research data . Dataset . 2018

TUT Acoustic Scenes 2017 Features

Heittola, Toni; Mesaros, Annamaria; Virtanen, Tuomas;
  • Published: 31 Jul 2018
  • Publisher: Figshare
Abstract
<p>TUT Acoustic Scenes features dataset consists of feature matrices extracted for 10-seconds audio segments from 15 acoustic scenes:&nbsp;</p> <ul> <li>Bus - traveling by bus in the city (vehicle)</li> <li>Cafe / Restaurant - small cafe/restaurant (indoor)</li> <li>Car - driving or traveling as a passenger, in the city (vehicle)</li> <li>City center (outdoor)</li> <li>Forest path (outdoor)</li> <li>Grocery store - medium size grocery store (indoor)</li> <li>Home (indoor)</li> <li>Lakeside beach (outdoor)</li> <li>Library (indoor)</li> <li>Metro station (indoor)</li> <li>Office - multiple persons, typical work day (indoor)</li> <li>Residential area (outdoor)</li...
Subjects
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Biochemistry, Cell Biology, Genetics, Neuroscience, Sociology, Science Policy, Plant Biology, 59999 Environmental Sciences not elsewhere classified, computational auditory scene analysis, acoustic scene classification, acoustic features
Funded by
EC| EVERYSOUND
Project
EVERYSOUND
Computational Analysis of Everyday Soundscapes
  • Funder: European Commission (EC)
  • Project Code: 637422
  • Funding stream: H2020 | ERC | ERC-STG
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Dataset . 2018
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Dataset . 2018
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Dataset . 2018
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Dataset . 2018
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research data . Dataset . 2018

TUT Acoustic Scenes 2017 Features

Heittola, Toni; Mesaros, Annamaria; Virtanen, Tuomas;