Robust audio fingerprinting using peak-pair-based hash of non-repeating foreground audio in a real environment

TLDR: A high-performance audio fingerprinting system used in real-world query-by-example applications for acoustic audio-based content identification, especially for use in heterogeneous portable consumer devices or on-line audio distributed system is proposed.

Abstract: In this paper, we propose a high-performance audio fingerprinting system used in real-world query-by-example applications for acoustic audio-based content identification, especially for use in heterogeneous portable consumer devices or on-line audio distributed system. In the proposed method, audio fingerprints are generated using a modulated complex lapped transform-based non-repeating foreground audio extraction and an adaptive thresholding method for prominent peak detection. Effective matching is performed using a robust peak-pair-based hash function of non-repeating foreground audio to protect against noise, echo, artifacts from pitch-shifting, time-stretching, resampling, equalization, or compression. Experimental results confirm that the proposed method is quite robust in various distorted conditions and achieves preliminarily promising accuracy results.