Scalability issues in an HMM-based audio fingerprinting

Audio fingerprinting technologies allow the identification of audio content without the need of external meta-data or watermark embedding. These audio fingerprinting technologies work by extracting a content-based compact digest that summarizes a recording and comparing them with a previously extracted fingerprint database. In this paper we present a fingerprint scheme that is based on hidden Markov models. This approach achieves a high compaction of the audio signal by exploiting structural redundancies on music and robustness to distortions thanks to the stochastic modeling. In this paper we present the basic functionality of the system as well as some results