Abstract
Automatic generation of play lists for commercial broadcast radio stations has become a major research topic. Audio identification systems have been around for a while, and they show good performance for clean audio files. However, songs transmitted by commercial radio stations are highly distorted to cause greater impact on the casual listener. This impact helps increase the probability that the listener will stay tuned in, but the price we have to pay is a severe modification in the audio itself. This causes the failure of traditional identification systems. Another problem is the fact that songs are never played from the beginning to the end. Actually, they are put on the air several seconds after their real beginning and almost always under the voice of a speaker. The same thing happens at the end. In this article, we present the RAA project, which was conceived to deal with real broadcast audio problems. The idea behind this project is to extract automatically an audio fingerprint (the soβcalled AudioDNA) that identifies the fragment of audio. This AudioDNA has to be robust enough to appear almost the same under several degrees of distortion. Once this AudioDNA is extracted from the broadcast audio, a matching algorithm is able to find its fragments inside a database. With this approach, the system can find not only a whole song but also small fragments of it, even with high distortion caused by broadcast (and DJ) manipulations.