Printed scores were once necessary for music listening. Until the 20th century, each musician playing a symphony would need his own notated sheet music in order to play a piece for every performance. Today, the bulk of music listening happens through recordings. Musicians only need to play a song correctly once in order for anybody to hear it anytime, anywhere.
But with the streamlined dissemination of digital music on the Internet, today’s listeners need guidelines for how to consume music just as badly as musicians once needed scores to produce new music. There is simply too much recorded music for any one person to keep track. Accordingly, “music discovery services”, which guide listeners through huge libraries of music, are beginning to emerge as a genuine growth industry.
Pandora, a leading music discovery service, famously began its Music Genome Project about a decade ago, a music classification method that numerically rates songs according to a long list of criteria and sorts songs by these “genetic” similarities. Pandora’s website generates playlist suggestions based on a minimal amount of input from listeners. Ideally, Pandora automatically can create personally tailored playlists that a listener didn’t have the knowledge or time to create.
Shortly before the Music Genome Project commenced, George Tzanetakis made Marsyas, an open-source toolkit for automatically classifying songs and entire libraries of music, among other applications. Pandora and Marsyas had similar aims - to intelligently sort music libraries to give listeners a way to find new artists and retrieve other qualitative information about music. Working at Princeton as a grad student with professor Perry Cook, who wanted to find a way of automatically sorting radio stations, Tzanetakis developed various library-browsing visualizations within Marsyas, including Genre Meter, which can respond live to sound sources and classify them (video demo.)
Pandora has taken off as a large-scale commercial venture, with more competitors like Spotify and Slacker in its wake. Tzanetakis’ Marsyas has remained known mostly only by academics and computer scientists. Regardless, Tzanetakis’ work addresses issues of music classification in a more radical and even prophetic way than Pandora: all of Marsyas’ “genes” are completely determined by computer automation. Tzanetakis’ contributions to the field of Music Information Retrieval (MIR, for short) have helped to push computers toward increasingly delicate interpretations of one of man’s most elusive forms of expression. Marsyas is available for free download and even has a free user manual.
Though songs in the Pandora database are weighed and sorted by algorithms, a board of experts determines the value of each “gene”. Recently, a New York Times reporter sat in with a group of Pandora’s experts listening to songs and then opining about how high a song scored in criteria like “emotional delivery”, “exoticism” and “riskiness”; as well as more concrete judgements on tempo, instrumentation and harmony.
By contrast, George Tzanetakis’ approach to music classification is completely automated. It needs no panel of experts or crowdsourced participants to complete an intelligently made, intuitively browsable library of music. It works based entirely on the audio signals themselves. Given merely a library of digital song files, George Tzanetakis’ automated classification techniques algorithmically organize songs according to a variety of criteria and present fun interactive ways to browse and compare music.