Statistical signal similarity check using symbolic data for power management on low capacity devices

Considering the success of mobile computing, realtime identification of Passive Acoustic Monitoring (PAM) data is now an emerging possibility. Despite computational evolution, analysis of raw acoustic data by complex algorithms requires considerably computing effort, therefore, consuming overly battery power. As battery power is a low resource in many environments, such as the sea, a very simple time-domain signal similarity filter is proposed in this paper. To accomplish that, the filter uses a richer representation of time-domain data created by symbolic data analysis. Taking this new data type and assuming environmental noise as a stationary process, a non-parametric statistical hypothesis test is applied to detect signal similarity over time. To evaluate overall processing time, a dataset of raw acoustic data acquired from PAM was used. In addition, to endorse accuracy and no data loss, all data were visually and acoustically searched for sperm whale clicks.