Skip to content
University of Jyväskylä | Thesis search
  • Language
    • Suomi
    • English
University of Jyväskylä | Thesis search
Language
  • Suomi
  • English
Advanced
  • Music mood annotation using se...
  • Cite this
  • Print
  • Export Record
    • Export to RefWorks
    • Export to EndNoteWeb
    • Export to EndNote
Music mood annotation using semantic computing and machine learning

Music mood annotation using semantic computing and machine learning

Show other versions (1)
Bibliographic Details
Main Author: Saari, Pasi, kirjoittaja
Format: Doctoral dissertation
Language:eng
Published: Jyväskylä : 2015.
Series:Jyväskylä studies in humanities, 243.
Subjects:
musiikki
tunteet
annotointi
laskentamenetelmät
koneoppiminen
mallintaminen
tägit
verkkoyhteisöt
sosiaalinen media
digitaalinen musiikki
music mood annotation
music emotion recognition
social tags
editorial tags
circumplex model
feature selection
genre-adaptive
semantic computing
audio feature extraction
Online Access:JYX-julkaisuarkisto / JYX Digital Archive
  • Description
  • Other Versions (1)
  • Staff View
Description
Item Description:Artikkeliväitöskirjan yhteenveto-osa ja 6 eripainosta.
Format:World Wide Web.

Similar Items

  • Music mood annotation using semantic computing and machine learning
    by: Saari, Pasi, kirjoittaja
    Published: (2015)
  • Semantic annotation and big data techniques for patent information processing
    by: Mwakyusa, Phesto Enock, kirjoittaja
    Published: (2017)
  • Semantic annotation and big data techniques for patent information processing
    by: Mwakyusa, Phesto Enock
    Published: (2017)
  • Feature selection for classification of music according to expressed emotion
    by: Saari, Pasi
    Published: (2009)
  • Developing and testing sub-band spectral features in music genre and music mood machine learning
    by: Prezja, Fabi
    Published: (2018)

Search Options

  • Search History
  • Advanced Search
  • New Items

Need Help?

  • Search Tips

Contact

  • University of Jyväskylä
  • Open Science Centre
  • Accessibility report