changeset 27:8d1d5829dc2a

rephrasing 4.3

--- a/musicweb.tex	Sat Apr 30 21:40:20 2016 +0100
+++ b/musicweb.tex	Sat Apr 30 21:45:56 2016 +0100
@@ -299,11 +299,12 @@
 
 \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. 
+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).
+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].
 
-Although similar to recommendation, 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''. Therefore we aim at creating links between artists based on stylistic elements of their music derived from a collection of recordings. This is in contrast with most recommender systems which operate on the level of individual music items and complements the social and cultural links discussed in the previous sections.
+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}