Gerhard Widmer, Simon Dixon, Tim Pohle, Elias Pampalk, Peter Knees,
"From Sound to "Sense" via Feature Extraction and Machine Learning: Derieving High-level Descriptors for Characterising Music"
, in P. Polotti and D. Rocchesso: Sound to Sense: Sense to Sound: A State-of-the-Art in Sound and Music Computing., 2007
From Sound to "Sense" via Feature Extraction and Machine Learning: Derieving High-level Descriptors for Characterising Music
Sprache des Titels:
Sound to Sense: Sense to Sound: A State-of-the-Art in Sound and Music Computing.
Research in intelligent music processing is experiencing an enormous boost these days due
to the emergence of the new application and research field of Music Information Retrieval
(MIR). The rapid growth of digital music collections and the concomitant shift of the music
market towards digital music distribution urgently call for intelligent computational support
in the automated handling of large amounts of digital music. Ideas for a large variety of
content-based music services are currently being developed in music industry and in the
research community. They range from content-based music search engines to automatic music
recommendation services, from intuitive interfaces on portable music players to methods
for the automatic structuring and visualisation of large digital music collections, and from
personalised radio stations to tools that permit the listener to actively modify and `play with'
the music as it is being played.
What all of these content-based services have in common is that they require the computer
to be able to `make sense of' and `understand' the actual content of the music, in the sense
of being able to recognise and extract musically, perceptually and contextually meaningful
(`semantic') patterns from recordings, and to associate descriptors with the music that make
sense to human listeners.