Abstract
In this paper we address the task of robustly detecting multiple bioacoustic events with repetitive structures in outdoor monitoring recordings. For this, we propose to use the shift-autocorrelation (shift-ACF) that was previously successfully applied to F0 estimation in speech processing and has subsequently led to a robust technique for speech activity detection. As a first contribution, we illustrate the potentials of various shift-ACF-based time-frequency representations adapted to repeated signal components in the context of bioacoustic pattern detection. Secondly, we investigate a method for automatically detecting multiple repeated events and present an application to a concrete bioacoustic monitoring scenario. As a third contribution, we provide a systematic evaluation of the shift-ACF-based feature extraction in representing multiple overlapping repeated events.