Automated acoustic detection of a cicadid pest in coffee plantations Abstract   South american countries are the largest coffee producers in the world. Nevertheless, Cicadidae, the colloquial term for cicadas, is one of the key pests responsible for dropping the production. Currently, there is no electronic device or autonomous technological resource commercially available for detecting certain species of cicadas in the crop, penalizing the farmers on the management of that insect. Thus, this article presents a novel algorithm implemented in a lowcost realtime plataform for the acoustic detection of cicadas in plantations. Based on the Bark Scale (BS), Waveletpacket Transform (WPT), Paraconsistent Feature Engineering (PFE) and Support Vector Machines (SVMs), the proposed technique was assessed with a database of 1366 recordings, presenting a value of accuracy of 96.41% for the distinction among cicadas and background noise, where the latter includes sounds from mechanical devices, birds, animals in general and speech, among others.

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.