Abstract:
At the core of the presented system is a highly
robust fingerprint extraction method which enables
searching a large fingerprint database with only
limited computing resources. Requirements for such
systems include robustness to a wide range of signal
distortions and availability of fast search methods,
even for large fingerprint databases. In this paper an
audio fingerprinting system is presented for song
identification. For the high dimensional audio
fingerprint data, audio fingerprint searching
algorithm were proposed: an audio fingerprinting
method based on DWE (Discrete wavelet entropy)
with timbral features (MFCC and FFT) and an
efficient indexing method for Audio fingerprint
database using the filtering approach, known also as
vector approximation approach which supports the
nearest neighbor search efficiently. Spectral subband
entropy is selected due to its resilience against
equalization, compression, and noise addition.
Region Approximation Blocks divides a highdimensional
feature vector space into compact and
disjoined regions. Each region will be approximated
by two bit-strings according to the RA-Blocks
technique.
