# HG changeset patch # User mariano # Date 1461927511 -3600 # Node ID 9866ea2d3b9ac1f738cc97f7757d3aac9e0d81eb # Parent d4d732b8b9cbd35d041e823af4329b46ec545b62 intro done diff -r d4d732b8b9cb -r 9866ea2d3b9a musicweb.tex --- a/musicweb.tex Fri Apr 29 09:37:29 2016 +0100 +++ b/musicweb.tex Fri Apr 29 11:58:31 2016 +0100 @@ -161,18 +161,16 @@ \end{abstract} \section{Introduction}\label{sec:introduction} -In recent years we have witnessed an explosion of information, a consequence of millions of users producing and consuming web resources. Researchers and industry have recognised the potential of this data, and have endeavoured to develop methods to handle such a vast amount of information: to understand and manage it, to transform into knowledge. Multimedia content providers have devoted a lot of energy to analysing consumer preference, in an effort to offer customised user experiences. Music stream services, for instance, carry out extensive analysis trying to identify patterns in user's listening habits, and researchers are striving to refine multimedia recommendation algorithms. There are two main approaches to music recommendation\cite{Song2012}: the first is known as \emph{collaborative filtering}\cite{Su2009}, which recommends music items based on the choices of similar users. There are, however, limitations in user-preference based systems: recommendations based solely on user preference can easily lead to a ''rich-club phenomenon''\cite{Zhou2004}, in which short-tail popular music is heavily reinforced whereas most of the music available online is ignored and remains unknown\cite{Celma2010}. The second most significant model of music recommendation is based on audio content analysis, or \emph{music information retrieval}. This analysis extracts low to high-level audio features such as tempo, key, metric structure, melodic and harmonic sequences, instrument recognition and song segmentation, which are then used to measure music similarity\cite{Aucoutourier2002}, to carry out genre classification or to identify the mood of the song\cite{Kim2010}. Music discovery websites such as Last.fm\footnote{http://www.last.fm}, Allmusic\footnote{http://www.allmusic.com} or Pandora\footnote{http://www.pandora.com} have successfully developed hybrid systems which combine both approaches. +In recent years we have witnessed an explosion of information, a consequence of millions of users producing and consuming web resources. Researchers and industry have recognised the potential of this data, and have endeavoured to develop methods to handle such a vast amount of information: to understand and manage it, to transform into knowledge. Multimedia content providers have devoted a lot of energy to analysing consumer preference, in an effort to offer customised user experiences. Music stream services, for instance, carry out extensive analysis trying to identify patterns in user's listening habits, and researchers are striving to refine multimedia recommendation algorithms. There are two main approaches to music recommendation\cite{Song2012}: the first is known as \emph{collaborative filtering}\cite{Su2009}, which recommends music items based on the choices of similar users. The second most significant model of music recommendation is based on audio content analysis, or \emph{music information retrieval}. This analysis extracts low to high-level audio features such as tempo, key, metric structure, melodic and harmonic sequences, instrument recognition and song segmentation, which are then used to measure music similarity\cite{Aucoutourier2002}, to carry out genre classification or to identify the mood of the song\cite{Kim2010}. Music discovery websites such as Last.fm\footnote{http://www.last.fm}, Allmusic\footnote{http://www.allmusic.com} or Pandora\footnote{http://www.pandora.com} have successfully developed hybrid systems which combine both approaches. +There are, however, limitations in user-preference based systems: most users listen to a very small percentage of the music available, the so called ''short-tail'', whereas the much larger ''long-tail'' remains mainly unknown\cite{Celma2010}. These systems will show a bias towards music that is already consumed by many listeners. Suggesting already popular music will increase the likelihood of it being recommended to new users, thus creating a ''rich-club phenomenon''\cite{Zhou2004} or what is known as ''cumulative advantage''. To many music lovers discovering ''new'' music, or music they weren't aware of, is an integral part of enjoying a musical experience, and appreciate expanding their musical taste. +Automatic musical discovery is a very challenging problem\cite{Jennings2007}. There are many different ways in which people are attracted to new artists: word of mouth, their network of friends, music magazines or blogs, songs heard in a movie or a T.V. commercial, they might be interested in a musician who has played with another artist or been mentioned as an influence, etc. The route from listening to one artist and discovering a new one would sometimes seem very disconcerting were it to be drawn on paper. A listener is not so much following a map as exploring new territory, with many possible forks and shortcuts. All these sources of information are in fact music metadata, data about the music data itself. Pachet identifies three types of musical metadata \cite{Pachet2005}: +\begin{enumerate} + \item Editorial metadata: information that is provided manually by authoritative experts. There is a wide range of potential producers of this kind of data, from record labels to collaborative schemes, as well as different kinds of data, from which musician played in which song to tour info, to artists' biography. + \item Cultural metadata: information which is produced by the environment or culture. This is data that is not explicitly entered into some information system, but rather is contained, and must be extracted from, other information sources, such as user trends, google searches, articles and magazines, word associations in blogs, etc. + \item Acoustic metadata: data extracted from audio files using music information retrieval methods. +\end{enumerate} +Musicweb is an application which offers the user the possibility of exploring editorial, cultural and musical links between artists. It gathers, extracts and manages musical metadata from many different sources and connects them in informative ways. This paper deals with the different ways in which musicweb gathers and shapes these resources and shapes them into high-level information. We will first review various knowledege-based web resources available to musicweb. We will then introduce the application itself and detail the architecture to analyse and extract data. Before the final conclusions and discussion of future work we will analyse the experience of interfacing with the application and how users can explore and discover new musical paths. -\begin{itemize} -\item NOTE: The problem with this is that it does not offer music \emph{discovery}! -\item Music metadata can improve this -\item Pachet's patper on metadata identifies three different kinds of music metadata - \begin{itemize} - \item Editorial metadata - \item Cultural metadata - \item Acoustic metadata - \end{itemize} -\end{itemize} @@ -208,35 +206,47 @@ \vspace{-1em}\begin{thebibliography}{5} % + \bibitem{Song2012} Y.~Song, S.~Dixon and M.~Pearce. \newblock A survey of music recommendation systems and future perspectives \newblock In {\em Proceedings of the 9th International Symposium on Computer Music Modelling and Retrieval}, 2012. - + \bibitem{Su2009} X.~Su and T. M. ~Khoshgoftaar. \newblock A Survey of Collaborative Filtering Techniques. \newblock In {\em Advances in Artificial Intelligence,(Section 3):1–19}, 2009. - - \bibitem{Zhou2004} - S.~Zhou and R. J.~Mondrag\'on - \newblock The rich-club phenomenon in the Internet topology - \newblock In {\em Communications Letters, IEEE}, 2004 + + \bibitem{Aucoutourier2002} + J. J.~Aucouturier and F~Pachet. + \newblock Music Similarity Measures: What is the Use. + \newblock In {\em Proceedings of the ISMIR, pages 157–163}, 2002. + + \bibitem{Kim2010} + Y.E.~Kim, E.M.~Schmidt, R.~Migneco, B.G.~Morton, P.~Richardson, J.~Scott, J.A.~Speck and D.~Turnbull. + \newblock Music Emotion Recognition: A State of the Art Review. + \newblock In {\em Proc. of the 11th Intl. Society for Music Information Retrieval (ISMIR) Conf}, 2010. \bibitem{Celma2010} O.~Celma \newblock Music Recommendation and Discovery:The Long Tail, Long Fail, and Long Play in the Digital Music Space. \newblock Springer Verlag, Heidelberg, 2010. - \bibitem{Aucoutourier2002} - J. J.~Aucouturier and F~Pachet. - \newblock Music Similarity Measures: What is the Use. - \newblock In {emProceedings of the ISMIR, pages 157–163}, 2002. + \bibitem{Zhou2004} + S.~Zhou and R. J.~Mondrag\'on + \newblock The rich-club phenomenon in the Internet topology + \newblock In {\em Communications Letters, IEEE}, 2004 - \bibitem{Kim2010} - Y.E.~Kim, E.M.~Schmidt, R.~Migneco, B.G.~Morton, P.~Richardson, J.~Scott, J.A.~Speck and D.~Turnbull. - \newblock Music Emotion Recognition: A State of the Art Review. - \newblock In {\em Proc. of the 11th Intl. Society for Music Information Retrieval (ISMIR) Conf}, 2010. + \bibitem{Jennings2007} + D.~Jennings. + \newblock Net, Blogs and Rock ’n’ Rolls: How Digital Discovery Works and What It Means for Consumers. + \newblock Nicholas Brealey Pub., 2007 + + + \bibitem{Pachet2005} + F.~Pachet + \newblock Knowledge management and musical metadata. + \newblock In {\em Encyclopedia of Knowledge Management}, Schwartz, D. Ed. Idea Group, 2005 \bibitem{FazekasRJS10_OMRAS2} G.~Fazekas, Y.~Raimond, K.~Jakobson, and M.~Sandler.