A robust characterization of audio signals using the level of information content per Chroma

Abstract

In this paper we propose a new technique to characterize audio-signals. We use Shannon's Entropy to estimate the level of information content per chroma and we show that involving entropy contributes for a more robust audio characterization. A new audio-fingerprint (AFP) based on this feature is proposed in this paper which we have called Entropy-Chroma Fingerprint (ECFP). Two approaches were considered to estimate entropy; the first assumes the spectral coefficients distribute normally, while the second, estimates its probability density function (PDF) with the Parzen Windows Estimation method. We compared the robustness of the ECFP against the Chromagram-Based Audio-Fingerprint (CBFP) which is determined using the Constant Q Transform (CQT). Three thousand and five hundred AFPs were determined from songs of several genres. A subset of 350 songs were severely degraded and searched for using excerpts of 5 seconds for that matter. The ECFP determined assuming gaussianity on the PDF turned out to be much more robust than the CBFP. The ECFP determined assuming gaussianity is much faster to process than both, the CBFP and the ECFP determined with Parzen Windows and still more robust.