Framework for Audio Fingerprinting based on Discrete Wavelet Entropy

Abstract

At the core of the presented system is a highlyrobust fingerprint extraction method which enablessearching a large fingerprint database with onlylimited computing resources. Requirements for suchsystems include robustness to a wide range of signaldistortions and availability of fast search methods,even for large fingerprint databases. In this paper anaudio fingerprinting system is presented for songidentification. For the high dimensional audiofingerprint data, audio fingerprint searchingalgorithm were proposed: an audio fingerprintingmethod based on DWE (Discrete wavelet entropy)with timbral features (MFCC and FFT) and anefficient indexing method for Audio fingerprintdatabase using the filtering approach, known also asvector approximation approach which supports thenearest neighbor search efficiently. Spectral subbandentropy is selected due to its resilience againstequalization, compression, and noise addition.Region Approximation Blocks divides a highdimensionalfeature vector space into compact anddisjoined regions. Each region will be approximatedby two bit-strings according to the RA-Blockstechnique