It uses both - the neural network creates it's own acoustic fingerprint database, which it then uses to perceive sound.
This shrinks the traditional acoustic fingerprinting data enough to be stored on mobile devices, while allowing for a low-power "always-on" identification offline.
It's analogous to a human being able to identify songs by remembering the chorus, just that the NN uses it's own features for both the memory and offline perception.
>In 2017 we launched Now Playing on the Pixel 2, using deep neural networks to bring low-power, always-on music recognition to mobile devices. In developing Now Playing, our goal was to create a small, efficient music recognizer which requires a very small fingerprint for each track in the database, allowing music recognition to be run entirely on-device without an internet connection.
It's analogous to a human being able to identify songs by remembering the chorus, just that the NN uses it's own features for both the memory and offline perception.
>In 2017 we launched Now Playing on the Pixel 2, using deep neural networks to bring low-power, always-on music recognition to mobile devices. In developing Now Playing, our goal was to create a small, efficient music recognizer which requires a very small fingerprint for each track in the database, allowing music recognition to be run entirely on-device without an internet connection.
https://ai.googleblog.com/2018/09/googles-next-generation-mu...