Automatic Song Identification in Noisy Broadcast Audio

E. Batlle, J. Masip, and E. Guaus (Spain)


Broadcast audio identification, mel-cepstrum coefficients,hidden Markov models, Viterbi, music information retrival.


Automatic identification of music titles and copyright en forcement of audio material has become a topic of great interest. One of the main problems with broadcast audio is that the received audio suffers several transformations before reaching the listener (equalizations, noise, speaker over the audio, parts of the songs are changed or removed, etc.) and, therefore, the original and the broadcast songs are very different from the signal point of view. In this pa per, we present a new method to minimize the effects of audio manipulation (i.e. radio edits) and distortions due to broadcast transmissions. With this method, the identi fication system is able to correctly recognize small frag ments of music embedded in continuous audio streams (ra dio broadcast as well as Internet radio) and therefore gen erate full play-lists. Since the main goal of this system is copyright enforcement, the system has been designed to give almost no false positives and achieve very high ac curacy.

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