hackrs instant analysis zu location diensten, good as always, n.b.: fußnote zu so was als si dervie nutzen und abschwiff zu: nicht mal popdiskurs ist so mainstream-unterwürfig wie web2.0-startup-diskurs (+++++)
While the pure science of music recommendation puts a heavy emphasis on novelty, Last.fm’s incomparable store of data about real listening preferences – as well as our own experience as music lovers – convinced us that it would be interesting to try a different approach. We noticed that listening to all-new music can be a bit heavy going. Similarly, just listening to your old favourites sometimes isn’t what you want either! A few shakes of the test tube in Last.fm’s radio and recommendations laboratory (known internally as the MIR or Music Information Retrieval team), and Mix Radio was born – a station that’s exactly that: a mix of the music you already know + some new recommendations!
von spar singen: ich bin eine ich-maschine ich bin eine ich-ag schmerz kennt keine freu(n)de und geld tut nicht weh ich bin eine ich-maschine ich bin eine ich-ag und ich bin skeptisch und das nicht ohne grund dreizehn grad westwind trübe stimmung jetzt fallen die engel so reden die bösen zungen und das soll es schon gewesen sein? nein danke, nein danke! das kann es noch nicht sein ich bin eine ich-maschine ich bin eine ich-ag: mach du doch den dreck weg!
We’ve been thrilled with the all support we’ve been getting from users who are helping us rate the tempo of music tracks in our Speedo experiment, thanks! Now we’d like to ask you to help us with another fun music experiment for a new project called Audio Flowers. We are currently doing some research into new techniques to measure structural change (or “complexity”) in rhythm, harmony and timbre directly from mp3 files. The measurements we take from a song are then summarised to produce a little image: an Audio Flower like the one below.
That’s why at Last.fm we’ve programmed our computers to listen to music. We fed them around 15,000 tracks from the UK singles charts between 1960 and 2008 and discovered some fascinating results we’d like to share with you. It all starts with the discovery that just before the middle of the 70s something in the data changed…