Music Retrieval and Recommendation: A Tutorial Overview
Sprache des Vortragstitels:
Englisch
Original Kurzfassung:
In this tutorial, we give an introduction to the field of and
state of the art in music information retrieval (MIR). The
tutorial particularly spotlights the question of music similarity,
which is an essential aspect in music retrieval and
recommendation. Three factors play a central role in MIR
research: (1) the music content, i.e., the audio signal itself,
(2) the music context, i.e., metadata in the widest sense, and
(3) the listeners and their contexts, manifested in user-music
interaction traces. We review approaches that extract features
from all three data sources and combinations thereof
and show how these features can be used for (large-scale)
music indexing, music description, music similarity measurement,
and recommendation. These methods are further
showcased in a number of popular music applications, such
as automatic playlist generation and personalized radio stationing,
location-aware music recommendation, music search
engines, and intelligent browsing interfaces. Additionally,
related topics such as music identification, automatic music
accompaniment and score following, and search and retrieval
in the music production domain are discussed.