Audio fingerprinting is a technology that allows for the identification and tracking of audio content. It is based on the idea that every audio recording, just like a fingerprint, has unique characteristics that can be used to identify it. These features are, mainly, the waveform, spectral envelope, pitch, the harmonics, overtones, and so on. Once a certain segment hast been “fingerprinted”, the system will be able to find it in another soundfile or stream.

 

But why would you want that? There are many reasons. Imagine there was an app that, hearing music through your mobiles microphone, would be able to tell you what song you are hearing, just by comparing the fingerprint it is building, with another one, already stored. Wouldn’t that be wonderful? Wait a moment…

 

This is one of the more known uses of audio fingerprinting in apps like Shazam or Soundhound. But there are many more. Imagine having the fingerprint of an explosion, car tire bursting or door slamming. And then applying that fingerprint to footage from a security camera in a shopping mall, in a street or in a building. Or cars honking and getting the sound feed from traffic cameras. The applications are virtually endless.

 

Or imagine a press agency that wants to know where certain declarations of one of their customers have been aired on TV or radio. And if they have done so in full, or just selected segments. And which ones exactly. The results of this kind of analysis is worth is weight in gold, because you can effectively assess the reach of certain peace of advertising, damage control declarations, etc.

 

There are different ways to perform audio fingerprinting, such as:

 

  • Spectral fingerprinting: It is based on analysing the audio’s frequency spectrum to generate a unique identifier.
  • Temporal fingerprinting: It is based on analysing the audio’s temporal structure to generate a unique identifier.
  • Hybrid fingerprinting: It combines both spectral and temporal fingerprinting methods to generate a more robust identifier.

 

And each one has its particular uses, that depend on what exactly you are are looking for. One of the main challenges in audio fingerprinting is dealing with variations in the audio recording. Factors such as background noise, compression, and different recording conditions can all affect the audio’s unique characteristics, making it more difficult to identify. To overcome this challenge, advanced audio fingerprinting algorithms use techniques such as robust feature extraction, error correction, and machine learning to improve the accuracy of the identification process.

 

One of the main applications of audio fingerprinting is in the field of music identification. This technology can be used to identify songs playing on the radio, in a store, or in a film, even if they have been remixed, edited or covered by another artist. This is useful for music industry professionals to track royalties and for consumers to discover new music. Or audio fingerprinting technology is also widely used in digital rights management (DRM) to protect against piracy. By embedding a unique audio fingerprint into a recording, copyright holders can track the distribution of their content and take legal action against individuals or organisations that distribute unauthorised copies.

 

And when we get to the Law Enforcement sector, this technology can be used to identify and track specific individuals or groups based on their voiceprints. Or identifying suspicious sounds in environments where they shouldn’t be. So you can see that audio fingerprinting is a powerful technology that allows for the identification and tracking of audio content. With its wide range of applications in music identification, audio surveillance, digital rights management and more, it is becoming increasingly important in today’s digital world. As the technology continues to improve and evolve, we can expect to see even more innovative applications and benefits in the future.