A Scalable Audio Fingerprint Method with Robustness to Pitch-Shifting

Audio fingerprint techniques should be robust to a variety of distortions due to noisy transmission channels or specific sound processing. Although most of nowadays techniques are robust to the majority of them, the quasi-systematic use of a spectral representation makes them possibly sensitive to pitch-shifting. This distortion indeed induces a modification of the spectral content of the signal. In this paper, we propose a novel fingerprint technique, relying on a hashing technique coupled with a CQT-based fingerprint, with a strong robustness to pitch-shifting. Furthermore, we have associated this method with an efficient post-processing for the removal of false alarms. We also present the adaptation of a database pruning technique to our specific context. We have evaluated our approach on a real-life broadcast monitoring scenario. The analyzed data consisted of 120 hours of real radio broadcast (thus containing all the distortions that would be found in an industrial context). The reference database consisted of 30.000 songs. Our method, thanks to its increased robustness to pitch-shifting, shows an excellent detection score.