Analytical Approach to MFCC Based Space-Saving Audio Fingerprinting System

Abstract

Audio fingerprinting is a smart technology to identify music contents relevant to the query by comparing the content-based hash (fingerprint) of the query to known hashes in the fingerprint database. For a million-song library, size of the fingerprint database restricts speedy and correct music identification. In this paper, we present a space-saving audio fingerprinting system based on Mel Frequency Cepstral Coefficients and analyze in detail. For a number of cepstral coefficients with 12 as default, the system yields a 2712-bit fingerprint for a 3-sec audio clip, a significant reduction in storage compared to the 8192-bit fingerprint of well-known Philips Robust Hashing method. Experimental results also show that the more number of input value for cepstral coefficients, the larger the fingerprint size. The choice of Mel coefficients also affects the identification performance of the system: 8 to 12 coefficients gives the best similarity rates while preserving robustness of the fingerprints to signal distortion such as pitch shifting.