Audio Fingerprint Extraction Using an Adapted Computational Geometry Algorithm

Abstract

This work presents an adapted version of the Computational Geometry Algorithm (CGA) used for the development of audio-based applications and services. The CGA algorithm analyses an audio stream and produces a unique set of points that can be considered to be the audio data "fingerprint". It is shown that this fingerprint is coding-independent, a fact that can render the proposed algorithm suitable for multiple purposes, including the categorisation of content identity and the identification of audio clips, hence providing support for the realisation of audio sorting/searching tasks and services. Additionally, based on specific novel applications and services, the overall algorithmic performance and efficiency characteristics of the CGA algorithm are discussed and analysed.

Efficient Audio Fingerprint Application Verification Using the Adapted Computational Geometry Algorithm

“…In our latest study, a novel audio content identification (matching) approach is presented, based on the significant reduction of the original spectral peaks enclosed in convex layer areas (Poulos et al, 2012). This work introduced audio-track identification through the use of computational geometry algorithms, where the problem of matching sample peaks with original peaks was addressed using an intersection technique between convex layers.…”

“…The same procedure is applied on the original (reference) signal x ref (n), producing the X ref (f) PSD vector of size N. Then, the CGA algorithm is applied on the derived PSD data, producing onion-like layers denoted in the case of reference signal as S. An example of such algorithmically constructed layers is graphically represented in Figure 1. Finally, a critical algorithmic parameter, the total depth of layers (or the k-depth value) is defined, following the algorithm described again in our latest study (Poulos et al, 2012). Finally, by algorithmically isolating the k-th inmost layer, we obtain the convex subset S xy that corresponds to the reference signal.…”

“…The above described method was realised via the use of the Computational Geometry Algorithm (CGA), a computationally efficient scheme of onion-like layers that results into unique frequency-domain representations of the innermost onion layer (Poulos et al, 2012). More specifically, the digital audio signal under identification (test signal), denoted here as x(n), is initially transformed in the frequency domain and represented in terms of its Power Spectral Density (PSD) X(f) via Bartlett's estimation.…”

“…The present paper can be considered to be a continuation of our latest research which lays the necessary theoretical foundation research on audio fingerprinting based on convex layer definition in the frequency domain (Poulos, Deliyannis, & Floros, 2012). In this work the experimental aspects of a novel algorithm for defining convex layer areas over audio signal spectral peaks as a track identification procedure are addressed in an attempt to standardize the identification process.…”

“…Section 2 briefly presents a synopsis of the Computational Geometry Algorithm (CGA) audio fingerprinting algorithm. This is an issue covered fully in our theoretical definition of the above algorithm published recently (Poulos et al, 2012) and the reader is encouraged to refer to this article for a detailed algorithmic and mathematical analysis. Next, Sections 3 and 4 provide extended experimentation cases based on a number of widely-employed application scenarios and present the results obtained using multiple forms of audio content as well as the statistical evaluation of the derived data.…”