An incremental technique for real-time bioacoustic signal segmentation

A bioacoustical animal recognition system is composed of two parts: (1) the segmenter, responsible for detecting syllables (animal vocalization) in the audio; and (2) the classifier, which determines the species/animal whose the syllables belong to. In this work, we first present a novel technique for automatic segmentation of anuran calls in real time; then, we present a method to assess the performance of the whole system. The proposed segmentation method performs an unsupervised binary classification of time series (audio) that incrementally computes two exponentially-weighted features (Energy and Zero Crossing Rate). In our proposal, classical sliding temporal windows are replaced with counters that give higher weights to new data, allowing us to distinguish between a syllable and ambient noise (considered as silences). Compared to sliding-window approaches, the associated memory cost of our proposal is lower, and processing speed is higher. Our evaluation of the segmentation component considers three metrics: (1) the Matthews Correlation Coefficient for point-to-point comparison; (2) the WinPR to quantify the precision of boundaries; and (3) the AEER for event-to-event counting. The experiments were carried out in a dataset with 896 syllables of seven different species of anurans. To evaluate the whole system, we derived four equations that helps understand the impact that the precision and recall of the segmentation component has on the classification task. Finally, our experiments show a segmentation/recognition improvement of 37%, while reducing memory and data communication. Therefore, results suggest that our proposal is suitable for resource-constrained systems, such as Wireless Sensor Networks (WSNs).