Often, sound clips being analyzed are not clean copies of a song. The song could be truncated, or it might be similar to a different song. This is where algorithms come in handy. The algorithm's job is to compare the fingerprints and determine if the incoming sound clip matches a song (or portion of a song) in the database within a certain range of probability.
The identification process is similar to the way forensics experts once matched a suspect's fingerprints to those found at a crime scene. Before sophisticated computer software and advanced methods for examining fingerprints became available, experts would look for points of similarity between different fingerprints. In most cases, the specialist would need to demonstrate at least 16 points of similarity for a print to be considered a match.
There is no standard probability range for content-recognition software. Most programs allow customers to adjust the level of similarity required to declare a match. For example, you could adjust the program so that it only brings back match results if the algorithm determines that there is a 95 percent or better chance it's a match. If the incoming clip doesn't fall in that range, it sends an error message to the user.
When the program determines a match, a partnered application can take over. The application might send information to someone who wants to know the title of a song, or it might flag a song on a Web site and e-mail the corresponding record company's legal department. Some record companies have used such software to scan file-sharing sites or to track content on Web sites that stream audio. The entire process of analysis and matching takes only a few seconds.
In the next section, we'll look at how video content presents different challenges than audio files.