Robust Detection and Pattern Extraction of Repeated Signal Components Using Subband Shift-ACF

Abstract

We propose a method for robustly detecting and extracting repeated signal components within a source signal. The method is based on the recently introduced shift autocorrelation (shift-ACF) which outperforms classical ACF in signal detection if a signal component is repeated more than once. In this paper, we extend shift-ACF to analyze the spectral structure of repeating signal components by using a subband decomposition. Subsequently, an algorithm for repeated event detection and extraction is proposed. An evaluation shows that the proposed subband shift-ACF outperforms detection based on classical cepstrum. We discuss several possible applications in the domain of sensor signal analysis, and particularly in audio monitoring.