James Parker2a1
Shazam introduced millions to the possibility that computational systems could identify sounds other than speech. In doing so, it provided many with a precedent and a language for approaching machine listening more generally. Researchers and journalists habitually describe some new technology as ‘Shazam for bats’ (Gallacher et al 2021), ‘Shazam for birds’ (Douglas 2017), or ‘Shazam for earthquakes’ (Than 2015). Indeed, in a notable reversal of the ‘rule’ that machine listening follows vision, references to ‘Shazam for artworks’ (Smartify), ‘Shazam for fashion’ (Lykdat), or ‘Shazam for plants’ (Plantsnap) are just as common. Shazam has become a dominant metaphor for computational identification as such.
Experiment
If you don’t have it already, download Shazam on a smart phone and spend some time with it. How well does it work? What are the app’s default settings? What is it trying to sell you? And what can you discern about how Shazam is trying to sell you? If you do have Shazam already, do you remember when or why you downloaded it? Now experiment with some of the alternatives for music identification. Try one built into your OS. How is the experience similar or different? What else, if anything, do you ‘Shazam’? Read about and test some of the apps below, which are all built on different technologies but recycle the same metaphor. How is the ecosystem of personal automatic identification evolving? What is the metaphor of Shazamification doing politically? How is it related, do you think, to other forms of identification like facial recognition for policing and security, or automatic gunshot recognition?
Resources
A short note on ShazamShazam for … everything?