LAST FM and the Music Multiverse


I’ve been going over a lot of my unpublished material. Found this still quite relevant paper from 2007 – written with Ross Harley and Andrew Murphie. There is a lot here about emerging media economies and analysis of metadata systems and user generated content. Probably needs updating now with some analysis of and amongst others but the key points still stand. And Spotify could perhaps learn the odd lesson here….


Proceedings of the 3rd Art of Record Production Conference 1
Queensland University of Technology, Brisbane Dec. 2007.


Mat Wall-Smith, Ross Rudesch Harley, and Andrew Murphie


School of English Media and Performing Arts, University of New South Wales; School of Media Arts,
College of Fine Arts, University of New South Wales



1 Introduction.


        The last few years have seen an explosion of online music services that challenge the dominant modes of digital music publishing and distribution, often claimed (in the mainstream press) to be”pioneered” by Apple iTunes. This period has also seen the development of software engines that filter and produce vast quantities of data in order to make specific suggestions based on user profiles and purchase histories (a strategy made popular by Amazon’s tracking of customer habits to build personalised recommendations).


        The rise in popularity of web-based music communities could well be seen as facilitated by, and situated within, the same technological framework as that of the iTunes or Amazon approach. However the rise of multi-user music communities can also be usefully situated within a slightly different set of histories. This alternative history foregrounds collective effort, open databases and community participation. Although there are many social networking sites that are experimenting with the way we use and engage with music in our everyday lives (eg Finetune [], Pandora [], MOG[], MyStrands [], iLike[], Myspace [ provides us with a really interesting example of how a particular approach to database systems can generate new ways to expand the listener’s sonic horizons. Rather than building a top-down expert system, has developed a bottom-up and agile system that opens up unexpected pathways to the discovery of music that listeners may not have found otherwise (1).


        The way, perhaps serendipitously, has managed to plug into a number of different ideas about listenership,taste, fandom, expert knowledge, databases and niche audiences makes it an interesting model for a vital and participatory engine of discovery and the basis of what we will call a new economy of affect (2). According to the work of Chris Anderson (2006), we are living in a ‘long-tail’ moment that is radically expanding the depth and breadth of both back-catalogues and new recordings. The massive proliferation and free availability of artists, genres and forms that characterises this moment presents a challenge to the traditional architectures of discovery upon which the established industry has survived. Although many punters and industry commentators are tempted to say that this sonic overload will simply be overwhelming, we see it more as the last piece of a bigger puzzle to do with the digitisation of music in general. Along the way, we want to ask whether the history of collectively-created music databases such as Gracenote sheds any light on the possible will face since its purchase by CBS earlier this year. Does Gracenote’s transformation from an open-content music database to a for-profit proprietary system raise concerns about the leveraging of community or fan knowledge for corporate gain?



2 Production, Distribution and Discovery


        In order to understand these developments, we need to see them as part of the radical transformation in the making and reception of music. The rise of the “hyper-active” listener (listeners have always been “active”, but maybe not so much as they are now) is part of major shift that has taken place in the making and distribution of music. For the purposes oft his paper we frame our work by way of the broad categories of production, distribution and discovery. Along with others in the field (such as Dave Kusek, Gerd Leonhard, and Andrew Dubber ) we see the main challenges and shifts at the moment to be taking place in the area of discovery (Kusek, Leonhard 2005). Although we will continue to see many new developments and work practices emerge, the area of production has already been massively transformed by digital methods, workflows and processes. Arguably with the introduction of the Alesis ADAT Digital Multitrack in 1991 which allowed 8 Tracks of 48 Khz 16bit digital audio to be recorded on affordable VHS tapes music production there has been an ongoing “democratisation” of music production. A newly realised market of ‘pro-sumers’ and ’project’ studios saw the development of relatively affordable and accessible production tools. While this market initially saw a marked increase in the mass production of affordable audio hardware characterised by the companies such as Behringer, the increasing facility and affordability of the home computer has seen this ’democratisation’ further ‘virtualise’ and accelerate (3).Software development increasingly replaces hardware based production platforms while at the same time recording practices diversify well beyond the once established economies and practices of commercial studio production.


        Paralleling this latter acceleration the web’s development and popular acceptance allowed for the distribution and massive circulation of music via a wide variety of digital formats and systems. The availability of a vast sea of audio recordings via the web, satellite, file-sharing, peer-to-peer,mobile networks, WiFi and a range of other networked technologies continues to transform the shape and nature of the recording industry. Though there are many battles still raging around the nature of rights, who should control them,and the conditions under which digital music should circulate, there is no question that we will continue to witness an explosion in the availability of all kinds of digital music, and that we will see a massive multiplication of artists and their recordings made available to the listening public.


This explosion of means to produce and distribute music presents as another set of challenges. As Roger Richards, head of Melbourne’s Extreme Records recently put it,


“Rapidly falling prices of music production equipment has resulted in a glut of music which conversely increases the need for new intermediaries, which is where the rise of online-recommender services, online social networks and niche blogs are replacing the old world of print media, face to face record stores and traditional distribution. Likewise, as the biggest bands make the final transition into ‘brands’ and their releases become ‘advertising’ for their expensive live shows, the impact upon niche music scenes in which ‘live’ performances are rare and releases act as social documents, is unknown.” (Richards and Chan, 2007. p. 9)


Although the idea of democratisation is problematic, we’re using it here to signal the extent to which access to the means of both production and distribution has shifted away from the monolithic control of a small number of corporations that built their ascendance and hedged their dominance on the economies of broadcast marketing and distribution. Those corporations continue to lose their grip on the means of production and distribution of commercial music. As music production and distribution drifts increasingly away from the orbit of the corporate recording industry, we see the status of music shift from product to process, from consumption to participation, from object to a network-of-relations.


        So under these new and constantly changing conditions, how do we navigate the “celestial jukebox” that so many new music pundits are keen to invoke — whether it’s Rick Rubin’s $19.95 per month cable subscription model(Strange 2007), or the music-as-water concept (see Tarkoswki 2006) that suggests music should be treated as a utility service? In trying to formulate one possible scenario for the future of the music industry in this context, SimonFrith and Lee Marshall paint the following picture that sets up our investigation:


        All music is now easily copyable and distributable by anyone with a computer, mobile phone or television.This has hit the major record labels hard — they’re much smaller operations than at the end of the twentieth century …[with] small independent labels serving localised niche markets. As a general development, music has become less mainstream. For consumers, while they freely copy recordings and download from P2P services, there is a feeling that music isn’t as important as it once was, that finding good music among all of the tunes swirling around is just too much hard work. And, with little possibility of gaining a deal with one of the old majors, and without taste shaping media such as national music magazines any more,musicians find it hard to make their work heard or valued.(Marshall and Frith 2004)


3 The development of CDDB, GraceNote and FreeDB.


        The rise of a networked music ecology can be framed as part of a larger history regarding the digitisation of music and the effects of this digitisation on the way we contain and categorise music as part of an extended information architecture. As recently as the early 1990s there was no metadata system for Compact Disc. Placing aCD into a computer didn’t automatically provide any metadata for that content. The metadata normally associated with the content via its physical packaging had yet to be reproduced in a form that would make its automated reconnection with the digitised content possible. In 1993,the amateur software developer Ti Tan wrote a Unix-basedCD player called XMCD. Not only could it play CDs on your computer, but it could utilise a disc recognition system to match CD track-listings with files kept on the user’s computer. Teaming with college-friend Steve Scherf, they developed an internet based service called Internet CompactDisc Database (CDDB), and enlisted an army of fans and volunteers to enter tracklists into the database. As CDs have no such metadata contained within them, the first person to submit the information to the database provides the information for the next person. “CDDB automatically gave information on any CD it knew, plus added new information as it came in from users.” (Fry 2001) By 1997, CDDB had become the default music database, and in 1998 CDDB was acquired by the media-player manufacturer Escient for just under a million dollars. From this point the CDDB system was licensed under the name Gracenote ( Gracenote has gone on to become the most dominant player in an industry concerned with the management and capitalisation of vast libraries of online media information.


        The CDDB system was, and still is, the result of the collective knowledge and labour of music fans who voluntarily enter track and CD metadata combined with information provided directly by record labels. Despite the collective production of Gracenote’s metadata, most of its revenue is provided by licensing access to the database.Many of the key online music services and hardware providers deploy Gracenote’s libraries. According to one source, CDDB has been used by more than 150 million people for a total of about six billion database searches.Clients include Apple, RealNetworks, Sony, Panasonic andHonda, who utilise the database in their propriatory technologies. ( Gracenote has grown to become one of the leading music recommendation systems for the music industry, and is focusing much of its current research on the problem of discovery. Their approach is based on “seeds” of user knowledge and what they refer to as “360 degree”personalised recommendations. Gracenote is the most ubiquitous music database on the planet but the capitalisation of a collective knowledge base has raised some serious questions and complications — both forGracenote and for other emerging systems that harness user-generated content for profit.


        The use of what was initially created collectively as public-domain knowledge for profit incensed many who had worked on producing data for the CDDB or had deployed it in the development of their software. As one representative of this view puts it, the “owner of CDDB and related trademarks and copyrights, as well as a number of software patents related to music metadata lookup … took a database that had been built by fans and turned it into a private company.” (anon.: n.d. 2008) In addition to these corporate appropriations of collective knowledge, Gracenote has also been engaged in litigation with competitors such asMusicMatch and customers like Roxio (who decided to include FreeDB [], the open content alternative to Gracenote, in their rip and burn software instead.)


        In the outcry that followed the commercialisation of CDDB, a number of determinedly open source and community database systems were developed. “FreeDB”remains the database of choice for developers and is considered a ‘clone’ of CDDB in terms of functionality.FreeDB is also licensed on a General Public License and is committed to remaining open and free. Both projects use a‘nearly’ unique identifier in order to identify CDs and their tracks according to their published order. This is of interest because the taxonomy that’s being used to handle meta-data in this model is a hangover from the CD-era. Songs are attributable only as tracks on albums according to the tracklisting system of the Compact Discs on which they are distributed.


4 MusicBrainz


MusicBrainz ( was another database project that developed out of a strong community reaction to the commercialisation of Gracenote. Musicbrainz provides an interesting juxtaposition to the more dynamic relational databases of ‘entertainment’ and ’social’ music sites such as and Pandora (simply in terms of populating a database). MusicBrainz is a Not for ProfitFoundation that uses two different ‘audio fingerprinting’ technologies to allow individual tracks to be identified and associated with appropriate metadata according to their audio characteristics. This approach removes any reference to the original CD architecture. Instead of identifying tracks according to their position within a defined playlist,fingerprinting technology allows the track to be identified by audio analysis. This is also one of the main areasGracenote — MusicBrainz’s arch enemy — is moving toward: audio waveform recognition. The non-commercial basis of MusicBrainz has resulted in relatively slow development cycles, and so projects like who harness user generated information are developing database back-ends at a much faster pace. Unlike MusicBrainz those systems are unhindered by an archaic infrastructure based on physical media and a mission that lacks relevance in a rapidly and ever-changing networked music ecology.MusicBrainz is stuck between its history as an open ‘metadatabase’ for cataloguing and attributing user-submitted data to the future development of a useful relational database.


5 A New Economy of Affect


        We have figured three steps that are barriers in realising a new music industry. The first barrier was production. The second was distribution. Both of these have been obliterated by cheaper and more professional home recording technology and an increasingly networked world.But this brings us to the issue of our third and final barrier to a new music industry: discovery.


        Radiohead’s much publicised subversion of the established mode of distributing commercial music by offering the download of their latest release In Rainbows at a price volunteered by the individual user was heralded in the popular media as a critical development in the battle of the record industry to stem the untempered exchange of digital media in the network environment. However Radiohead can afford to go it alone. The success of In Rainbows rode on a surplus that is in part a product of the machinery the band would subvert. The fact that Radiohead’s music is ubiquitous and that Radiohead is an internationally recognised brand are all testament to the broadcast economies upon which the record industry the broadcast depends. For this reason the In Rainbows experiment actually tells us very little about the new music ‘multiverse’.All this pseudo-independence tells us is that the members and management of Radiohead have taken control of a developed and nearly universally recognised brand. The emerging or developing artist obviously has no facility. It is discovery, rather than production or distribution that provides the major challenge for emerging artists operating in the contemporary mediascape. It is discovery rather than distribution that is the crucial battleground between established and emerging forms of music industry. We argue that the new ecology of music to which we have referred demands a new economy of affect to augment (if not entirely supplant) a broadcast economy that has proven incapable of effectively capitalising on the massive proliferation and distribution of networked music.


        In this context we can simplify and reduce ’economy’ in the ‘economy of affect’ by describing it as the relationship between supply and demand. This relationship is far from given — in fact the new music economy illustrated by Chris Anderson’s The Long Tail indicates that a surplus on the supply side can drive demand rather than diminish it (2). Anderson’s research illustrates that the more music that is made available, the more it gets consumed.The digital and networked distribution of media removes all blocks to maintaining and making accessible a complete catalogue of media, namely retail space and the economies of industrial production. Anderson’s research shows that artificially manipulating supply by limiting and controlling access to the means of production and marketing is false economy. However the business model on which the recording industry depends is built on the industrial production of an affective surplus. Broadcast technology lent itself to the mass/industrialised production of/capitalising upon affective surplus or excess. The record industry survives on the manipulation of affect on a broadcast scale. Investment in the production, marketing,and distribution of a small stable of recording stars via the broadcast media thus translates to the form of sales and distribution that Anderson illustrated in The Long Tail with10% of albums responsible for 80% of profits. We buy (or otherwise appropriate) what we hear. Until now the economies of the music industry — from the modes of production, through to distribution — have allowed those industry stakeholders to control the bulk of what we hear and to ‘focus’ demand in the process. Anderson’s book suggests a revaluing of the ‘long tail’. The ‘long tail’ is the mass of recorded music that was inaccessible or out of print-prior to the affordances of network distribution. While its true that the digital/network distribution of media allows for retailers such as Amazon or iTunes to effectively capitalise on the sheer length of the long tail, there is no impetus forApple through iTunes (in partnership with the record labels)to further undermine the established economy. While there has been as massive uptake of the network as a means for sharing music there has been little change in the way we discover music — the broadcast media remains the principle conduit for the production of the affective surplus that drives listeners to purchase media.


        ’Affect’ here refers to the things that move the body. It refers to the raw modulation that precedes thought:the stuff that moves the body to think, but also to fight, or to flee, or to dance. Affect precedes the identity formations that are the lifeblood of constraints held in place by external regulation and commercial promotion (including engagements via advertising). So even a more normative analysis of the way in which identity formation in these contexts might be used or constrained by standard technical,legal or commercial means would have to include the struggle of all of these with the very different “logic of affect” (Guattari 1995. p. 9). Our suggestion here is that the new forms of discovery found on sites such as are much more in tune with this “logic of affect” than they are with what Guattari opposes as the “logic of delineated sets”(1995. p.9), of identity even. We are interested in what happens to the logic of delineated sets, of fixed,hierarchically imposed legal, technical and commercial constraints when these are so thoroughly challenged or contaminated by new enabling of the logics of affect. The enabling is of course provided in large measure by the new intensities of relation (no longer vertical but horizontal relation) made possible, not by technical constraint but by amore fluid technical enabling. The term “economy of affect”refers to the fact that affective intensities or excesses themselves exhibit an economy of circulations and exchanges. The history of recorded music has been shaped by the way key technologies have afforded and modulated these economies of circulation and exchange.


        Here we are suggesting that the affordances of digital distribution indicate the possibility for a far more decentralised and dynamic — indeed a more effective —economy of music distribution that has far reaching implications for the way we organise, produce, share, and interact via data in general. There is, however, a more fundamental and more political argument at work here: the affordances of a network ecology and digital technologies have irrevocably altered (and continue to alter) the dynamic of the musical mulitverse — music is no longer constrained by the logics and economics of a broadcast technics and the economies of affect to which it lent itself. The new ecological order demands new engines of discovery. The facility for these engines of discovery to effectively traverse,manipulate and capitalise upon a newly dynamic multiverse,to realise a more appropriate economy of affect, will determine the future vitality, the future industry, of the development the development musical expression. Here we would like to look at two divergent attempts to traverse and to capitalise upon the emerging music multiverse. Both systems are based on the development of relational database systems in order to create audio streams of recommended tracks. The first example is The Music Genome Project and its Pandora front-end and the second is, a dynamic user generated system.


6 Pandora


        The Music Genome Project is a database of music metadata classified by paid musicians who analyse each track manually. The genome trope sets the scene for theProject’s approach — there is an inherent assumption that the experience of music can be reduced to its component parts and qualities: tempo, rhythmic qualities,instrumentation, personnel and in excess of 400 other carefully defined types that are assigned by The MusicGenomes expert taxonomists. It is (usefully) a very full expression of the “logic of delineated sets”. “The MusicGenome as the whole DNA of music” trope suggests user experience is not subject to change. The effectiveness ofThe Music Genome is thus subject to the ability of its logging team to keep up with the almost limitless catalogue of music that currently exists and which, thanks to the changes we’ve noted in production and distribution, is increasing in density at an exponential rate. Pandora is the streaming radio side of The Music Genome project. It works by deploying the Genome metadata to make connections between tracks based on those 400 qualities and streams those recommendations while providing for user input in order to fine-tune its selections and to note anomalies.


        Pandora’s most endearing quality is its ability to make connections that transcend the myriad qualities that groups distinct musical expression into movements and histories. Pandora is likely to follow Wolfmother with BlackSabbath, the Kinks with the White Stripes, recognising kin and influence across generations and cultures. That said,there is nothing quite so off-putting as a recommendation engine that begins by asking you for your favourite artists but then cannot locate them in its database. On the other hand, there is nothing quite so engaging as inputting the three most fringe artists you can think of and having them return a result. With Pandora our experience is the former;with the latter. The Music Genome Project is the basis for what is called an ‘expert system’ approach to an ’intelligent’ recommendation service. The problem with expert systems, however, is that they are obviously predicated upon, and therefore tend to work very well in, a carefully defined territory where all data-objects can be recorded and all there possible movements mapped. This isa problem realised in a variety of other fields including many approaches to modelling intelligent systems such as AI and A-life. Once you move outside the map, ‘expert systems’ tend to collapse (Clark 1997. pp.2-3, 58-59; Brooks 2002.pp.30-31). The problem with mapping out the so called ’long tail’ of the new music ecology is that it keeps getting longer, growing mutant offshoots that interconnect in weird and wonderful ways.


        The Pandora/Music Genome engine makes some key assumptions that limit the dynamism inherent to new modes of discovery, and we’d argue the effectiveness of its dynamic play-listing. For example, it assumes that the style of music that I am listening to at any one moment is the one that I want all my listening to be guided by. That is to say it assumes my listening habits are orderly and predetermined.It assumes that if I had listened to John Butler, I’m likely to want to listen to Xavier Rudd next, and Ben Harper after that — a rather tedious prospect if it was to continue indefinitely. This may indeed make Pandora an effective engine for a personalised radio stream for those who prefer a well-traveled path, but as a model of discovery it shows the limitations of an expert system to confuse the always-incomplete map with the territory.


        As a final note on Pandora and The Music GenomeProject we’d like to reinforce the point that music culture is not genomic. We are not even sure that music itself is; but music cultures definitely aren’t. Assuming that my music listening habits can be mapped and utilised in this way is akin to suggesting that our DNA can explain all the contingencies of human cultural development. In both cases we are negating the intense experiences and interactions —what we might call ‘relationality’ — that draw out and fold forward a multitude of possible futures from any particular quality or instance. This approach suggests that music and human culture is vital and in a state of continual flux and proliferation.



38 began as two projects founded by two distinct groups. The first project was Audio Scrobbler,which allowed users to populate a database and to receive recommendations within popular audio software and portable music players. The second project was,which was based on dynamic user-generated streaming.These two projects combined to form the current system, which we argues provides a totally new model for‘engines of discovery’. Since 2005 the two previously distinct projects have been completely integrated under banner. The real-time streaming adds a recursive intensity to the generation of the combined andAudio Scrobbler database. The listener’s interactions with the stream continuously fold back into the database realising new connections and new possible streams. For this reason the system feels very live and active as a listening experience. An open API provided for the integration of the playlist history of the iPod and iTunes to be ’scrobbled’ automatically. The ease of uploading and augmenting metadata according to experience combined with the ubiquity of compatible music software and hardware, meant that Audio Scrobbler and were able to develop a massive and completely dynamic relational database based on a huge and actively productive community of’ scrobblers’.

39 represents a new model for the development of an alternative economy of affect, and stands in stark contradistinction to the traditional broadcast/mass-production model of the music industry. Firstly, Audio ’scrobbling’ refers to the automated submission of metadata about specific tracks played on your computer or portable player to’s database.’s database is populated with this data (as well as the data that exists as a function of its own streamable catalogue). Unlike Pandora, this means that there are no missing links or spaces in’s catalogue. The moment I ‘scrobble’ a track it is added to the database. If that track is badly named or has erroneous metadata or is simply an unreleased private jam it will still get scrobbled. However that errant data won’t link to any other user’s playlist. It will thus fall off the radar via a process of natural attrition; it will never be recalled in the dynamic network that is the collective consciousness of system. The crucial thing to note here is a big difference in the type of metadata that privileges. It is not concerned with metadata based on a strict and static system of categorisation, but rather on data derived from the communities’ experience of, and reaction to, the data object. It treats thethis ’social music as an active component in a vital community rather than seeing it as a given object in a static reference system. Crucially, it serves the whole musical spectrum equally and allows connections to be made between diverse music types regardless of genre or market position. My recommendations can thus move from Merzbow, to Cole Porter, to Moving Ninja simply because those tracks are connected via people’s active listening experience.


        The original power in the engine was its ability to move beyond the performance aspect that usually operates in any online profiling engine. By ’scrobbling’ data automatically, based on the users actual listening history,the engine avoided the usual quirks of a more ‘reflexive’ self-profiling. On the early scrobbler integrations (before the development of social bookmarking features) you ‘were what you listened to’. The addition of the simple schema of ‘skipped’, ‘loved’ and ‘banned’ options meant that the data was being ’scrobbled’ according to an affective reaction to a track. The simplicity of that schema reduced the tendency to ‘intellectualise’ this reaction. On, tags were effectively delimited to those three ‘affective’ categories— ‘love it’, ‘hate it’, ‘not sure’. The relational power of was built on the connectivity that such a delimited schema provided between listener profiles, and consequently between artists, between individual tracks, etc. The power of lies therefore inits ability to move beyond the assertion of a particular taxonomy or typology. The addition of groups, friends (as opposed to listening ‘neighbours’) and tags reintroduced the ’subjective’ structuring of musical relations. For many, this ’social’ networking aspect has become the principle element of This hasn’t yet threatened the diversity of playlists, but has instead added a degree of peer influence to profile generation. Cliques tend to emerge and people increasingly move into ‘closed’ neighbourhoods with the ability to listen to another member’s or group’s stream bychoice.


        It is for this reason that we earlier referred’s successes as an innovative engine for discovery as ’serendipitous’. The addition of social networking and individual profile features appears to indicate a return to the ’identity formations’ that we referred to as being the lifeblood commercial promotion that the ‘affective taxonomy’ effectively routed around. That said, the combination of these two original features — an open user-generated database system and a simple affective taxonomy— provide for an engine of discovery that really has changed the way we listen to music. Together they provide for an effective distribution of intensity — what Andrew Murphie (2005) refers to as akin to the ‘dynamic of the live’. is only controlled by people actually listening and by people you know, or get to know, as active listeners. This allows for very immediate listening experience that adds a certain veracity of experience to the playlists. The experience that feeds is a vital and immediate social experience and marks the dynamic remembering of music asa generative social practice.


8 Conclusion


        In conclusion we’d like to suggest that offers a model for a system of discovery that is agile enough to deal with the new dynamism that constitutes the music ecology of the networked world. Moreover it allows people to share the experience of listening intelligently (a kind of affective intelligence). Though this might seem counterintuitive,this actually means removing much of the intent and the performance that compromises the metadata of many user generated systems (which obfuscate the realisation of a more embodied/affective knowledge).


        There are numerous problems with the system, and we do not mean to laud it uncritically. It does tend to trap mainstream listeners in mainstream listening circles, for example. At the same time however, listeners on the fringe are more often than not those actively populating the database and making new connections between might also be criticised for its centralisation and capitalisation of metadata, in a manner that hands over the information generated by a community of users to a corporate concern (admittedly voluntarily and for a trade-off in terms of functionality). The question remains whether having been sold to CBS, one the original powers in broadcasting, will continue to provide such an open engine of media discovery and an uncontained but very industrious economy of affect. It is both plausible and realistic to imagine a system like that is opensourced and physically distributed so that the rights of artists and consumers alike can be ensured rather than freely handed over to a corporate intermediary. Music has been ’reinvigorated’ by dynamic media systems such as those discussed in this paper. Despite the big stakeholders of the music industry crying foul, we’ve found that thanks , eMusic, and blogs like Cyclic Defrost, the last few years have been the most exciting and most vital years of listening of our lives




(1) This discussion has links to the ongoing debates concerning”folksonomies”. For an interesting discussion of this with regard to the social networking photography site, Flickr, see Simons, 2008.
(2) The notion of an economy of affect has an extensive if at times occluded history, beginning at least with psychoanalysis (seeGreen, 1999: 186ff). In cultural studies, some of the key discussions that have occurred can be found in the work of Jean-Francois Lyotard (1993), Gilles Deleuze (for example,2001) and Félix Guattari (for example, 1996), RaymondWilliams and Lawrence Grossberg (on Williams and Grossberg, see Harding and Pribra, 2004; Grossberg, 1997),Brian Massumi (2002) and the recent take up of the work ofSilvan Tomkins by cultural theorists such as Eve Kosofsky Sedgwick (1995, 2003). Interesting recent discussions of affective economies can be found in Sianne Ngai’s UglyFeelings, especially chapter 1 (pp38ff), and in Patricia Ticineto Clough’s 2007 edited collection on the “affective turn”. See also Scannell, 2001, on affect and mp3 sharing.
(3) Virtualise here refers to the computer engineering usage of thet erm that sees the software universalised across underlying platforms. It might well be argued that there is also a virtualisation occurring in the philosophical sense as well as this virtualisation of once hardware-rendered processes alsol eads to a diversification of developers and the tools they produce.
(4) See Massumi on intensity as associated with “nonlinear processes” (2002: 26) and as “unassimilable” (27) to standard”narrative or functional lines”. See Wall-Smith (2008) on tagging and affective intensity.




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