Animal Call Recognition with Acoustic Indices: Little Spotted Kiwi as a Case Study Longduration recordings of the natural environment are very useful in monitoring of animal diversity. After accumulating weeks or even months of recordings, ecologists need an efficient tool to recognize species in those recordings. Automated species recognizers are developed to interpret fieldcollected recordings and quickly identify species. However, the repetitive work of designing and selecting features for different species is becoming a serious problem for ecologists. This situation creates a demand for generic recognizers that perform well on multiple animal calls. Meanwhile, acoustic indices are proposed to summarize the structure and distribution of acoustic energy in natural environment recordings. They are designed to assess the acoustic activity of animal habitats and do not have discrimination against any species. That characteristic makes them natural generic features for recognizers. In this study, we explore the potential of acoustic indices being generic features and build a kiwi call recognizer with them as a case study. We proposed a kiwi call recognizer built with a Multilayer Perceptron (MLP) classifier and acoustic index features. Experimental results on 13 hours of kiwi call recordings show that our recognizer performs well, in terms of precision, recall and F1 measure. This study shows that acoustic indices have the potential of being generic features that can discriminate multiple animal calls.

Comparing Performances of Five Distinct Automatic Classifiers for Fin Whale Vocalizations in Beamformed Spectrograms of Coherent Hydrophone Array A large variety of sound sources in the ocean, including biological, geophysical, and manmade, can be simultaneously monitored over instantaneous continentalshelf scale regions via the passive ocean acoustic waveguide remote sensing (POAWRS) technique by employing a largeaperture denselypopulated coherent hydrophone array system. Millions of acoustic signals received on the POAWRS system per day can make it challenging to identify individual sound sources. An automated classification system is necessary to enable sound sources to be recognized. Here, the objectives are to (i) gather a large training and test data set of fin whale vocalization and other acoustic signal detections; (ii) build multiple fin whale vocalization classifiers, including a logistic regression, support vector machine (SVM), decision tree, convolutional neural network (CNN), and long shortterm memory (LSTM) network; (iii) evaluate and compare performance of these classifiers using multiple metrics including accuracy, precision, recall and F1score; and (iv) integrate one of the classifiers into the existing POAWRS array and signal processing software. The findings presented here will (1) provide an automatic classifier for near realtime fin whale vocalization detection and recognition, useful in marine mammal monitoring applications; and (2) lay the foundation for building an automatic classifier applied for near realtime detection and recognition of a wide variety of biological, geophysical, and manmade sound sources typically detected by the POAWRS system in the ocean.