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# Overview

Audio fingerprint (AFP) identification is the process of analyzing audio data to identify occurrences of known audio clips, such as specific pieces of music or particular adverts. This process is useful for detecting when specific adverts occur, to check for copyrighted music being played, to pick out commercial jingles, and so on.

The first step is to build a database of the audio clips to identify. Each clip is represented in the database by a sequence of distinctive features. IDOL Speech Server can analyze incoming audio to detect occurrences of the stored clips. IDOL Speech Server compares the target audio to the database clips and identifies sections that closely resemble the database clips.

IDOL Speech Server supports two approaches to performing audio fingerprinting:

- The first approach is based on acoustic landmark matching. This approach is highly scalable, and very fast.
- The second approach is based on acoustic template matching. Although this approach is not quite so scalable (suitable for databases that are tens of hours in size, not hundreds), it is more robust to reverberation, echo, and other acoustic distortions.

The following section covers only the first approach in detail. However, for all the audio fingerprinting tasks except `AfpDatabaseOptimize`, you can set the `AfpMode` parameter to `robust` to use the template based approach. For more information, see the IDOL Speech Server Reference.

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