Abstract:
Audio-Fingerprints (AFPs) are essential
characteristics of digital audio streams used to score
the perceptual similarity between audio signals.
Audio-fingerprinting systems extract features from
the signal normally on a frame by frame basis. In
this paper, a robust audio-fingerprint (AFP)
approach is developed based on spectral entropy in
wavelet domain. To extract the fingerprints of a
song, Shannon’s entropy is determined from the
spectral coefficients of each one of the first 24
critical bands according to the Bark scale. The
performance of this AFP system is evaluated on a
music database containing various genres. The
robustness of the system is validated through
degraded music signals in 4 different ways: white
noise addition, lossy compression, lowpass filter and
resampling.
