# HG changeset patch
# User mariano
# Date 1462050281 -3600
# Node ID eb58bf95743b476617a5aee5711fd5dc34287e16
# Parent  b65b3d98406604bc6e87991572a22173aaf28d15# Parent  8d1d5829dc2ad137d81fd01e3cece2294bb96917
after merge

diff -r b65b3d984066 -r eb58bf95743b musicweb.tex
--- a/musicweb.tex	Sat Apr 30 22:03:55 2016 +0100
+++ b/musicweb.tex	Sat Apr 30 22:04:41 2016 +0100
@@ -329,7 +329,15 @@
 
 
 
-\subsection{Content-based information retrieval}\label{sec:mir}
+\subsection{Content-based linking}\label{sec:mir}
+
+Content-based Music Information Retrieval (MIR) [Casey et.al. 2008] facilitates applications that rely on perceptual, statistical, semantic or musical features derived from audio using digital signal processing and machine learning methods. These features may include statistical aggregates computed from time-frequency representations extracted over short time windows. For instance, spectral centroid is said to correlate with the perceived brightness of a sound [Schubert et.al., 2006], therefore it may be used in the characterisation in timbral similarity between music pieces. More complex representations include features that are extracted using a perceptually motivated algorithm. Mel-Frequency Cepstral Coefficients for instance are often used in speech recognition as well as in estimating music similarity. Higher-level musical features include keys, chords, tempo, rhythm, as well as semantic features like genre or mood, with specific algorithms to extract this information from audio. 
+%
+Content-based features are increasingly used in music recommendation systems to overcome issues such as infrequent access of lesser known pieces in large music catalogues (the ``long-tail'' problem) or the difficulty of recommending new pieces without user ratings in systems that employ collaborative filtering (``cold-start'' problem) [Celma, 2008].
+
+In this work, we are interested in supporting music discovery by facilitating a user to engage in interesting journeys through the ``space of music artists''. Although similar to recommendation, this is in contrast with most recommender systems which operate on the level of individual music items. We aim at creating links between artists based on stylistic elements of their music derived from a collection of recordings and complement the social and cultural links discussed in the previous sections.
+
+
 
 \section{Discussion}\label{sec:discussion}