To solve this problem, the music team within the Applied Research group at Gracenote developed a new recognition system which not only compensates for audio interference but can also handle audio variations such as the ones described above. Even from a known artist, a live performance typically exhibits audio variations such as changes in key (e.g., the artist cannot sing as high as she/he used to), tempo (e.g., the band plays faster than usual), or instrumentation (e.g., an acoustic guitar replaces an electric one). While MusicID and StreamFP are fast and accurate audio fingerprinting systems, they can only identify known recordings and will not work with alternate versions such as live song recordings. While MusicID is based on the well-known Philips algorithm (one of the earliest audio fingerprinting systems), a more efficient system dubbed StreamFP is currently in development.įigure 1: Overview of an audio fingerprinting system.
Here is how Gracenote’s audio fingerprinting system works: Even with static, noise, and other audio interference, fingerprints allow for fast and accurate music recognition.
To identify music, MusicID uses audio fingerprints - compact and unique digital song identifiers. Running in hundreds of millions of car infotainment systems, sound systems, laptops, smartphones, and other devices throughout the world, MusicID resolves over 20 billion queries every month using its 200 million+ track reference database. Using Audio Fingerprinting to Identify MusicĪs an early pioneer in CD recognition, Gracenote today is probably best known for its MusicID software which can automatically and rapidly identify songs and return metadata such as artist name, track name, and album cover art.
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