Audio fingerprinting based on local energy centroid

Abstract

Audio fingerprint is an effective representation of an audio signal using low-level features and can be used to identify unlabeled audio based on its content. In this paper, we introduce a robust audio feature, local energy centroid (LEC), which can represent the energy conglomeration degree of the relative small region in the spectrum. Our audio fingerprint is generated based on the LEC feature which is conducive to enhance the robustness of system. In audio retrieval processing, an improved scoring strategy is proposed to resist the linear speed change. Experimental results show that the new fingerprinting system is quite robust in the present of noise and the proposed method can achieve satisfying recognition accuracy.

Daubechies Wavelets Based Robust Audio Fingerprinting for Content-Based Audio Retrieval

“…Existing schemes can be classified into three categories, i.e., time-domain based, transformdomain based and compressed-domain based. For example, a robust audio feature called local energy centroid (LEC) was proposed to represent the energy conglomeration degree of the relative small region in the spectrum (Pan et al, 2011), while a robust audio fingerprinting algorithm in the MP3 compressed domain was proposed with high robustness to time scale modification (Zhou & Zhu, 2011). Among existing transform-based audio fingerprinting schemes, the schemes based on the wavelet transform are very popular, since the wavelet transform or more particularly the discrete wavelet transform is a relatively recent and computationally efficient technique for extracting information about non-stationary signals like audio.…”