Spotify buys Imperial audio recognition start-up

Sonalytic, an audio detection start-up founded by an Imperial post-doc, has been bought by Swedish music streaming giant Spotify.

The company, which was founded by Martin Gould when he was a post-doctoral researcher in the Department of Mathematics last year, has developed a next-generation audio identification technology that uses machine learning to identify, monitor, and discover music. 

Sonalytic can recognise songs, mixed content and audio clips as short as one second in length, and is robust to changes in pitch and tempo, the addition of background noise, filtering, compression, looping and other distortion. It can help rights-holders monitor the usage of your copyright-protected material, or help users discover music they may like based on their previous music taste. 

The team were finalists in Imperial’s Venture Catalyst Challenge last year. They are now joining the world's most popular music streaming service, which has 50 million paying users worldwide.

Musical advances

Spotify said: “We’re happy to announce that Sonalytic is joining the Spotify family. The Sonalytic team is passionate about creating technology to improve the music ecosystem for artists and fans. Their advancements in audio feature detection will be used in several ways to advance Spotify’s mission: from improving Spotify’s personalized playlists to matching songs with compositions to improve our publishing data system.” 

Speaking last year, the team said:  “Even though people are listening to more music than ever before, the associated revenues have been in sustained decline for more than a decade. Ultimately, artists — and particularly smaller, independent artists — are losing out, to the extent it’s necessary for many musicians to work a second job to fund their passion.

"We believe that by helping to monetize music in new and innovative ways, this broken model can be fixed. Music is our passion, and we truly believe that technological innovations can be the solution to this difficult problem”.