Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Automatic Segmentation of Acoustic Musical Signals Using Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Introduction and overview: a sample of music information retrieval approaches
Journal of the American Society for Information Science and Technology - Music information retrieval
Two-layer automatic sound classification system for conversation enhancement in hearing aids
Integrated Computer-Aided Engineering
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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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 so-called 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.