Western Mediterranean wetlands bird species classification: evaluating small-footprint deep learning approaches on a new annotated dataset

The deployment of an expert system running over a wireless acoustic sensors network made up of bioacoustic monitoring devices that recognise bird species from their sounds would enable the automation of many tasks of ecological value, including the analysis of bird population composition or the detection of endangered species in areas of environmental interest. Endowing these devices with accurate audio classification capabilities is possible thanks to the latest advances in artificial intelligence, among which deep learning techniques excel. However, a key issue to make bioacoustic devices affordable is the use of small footprint deep neural networks that can be embedded in resource and battery constrained hardware platforms. For this reason, this work presents a critical comparative analysis between two heavy and large footprint deep neural networks (VGG16 and ResNet50) and a lightweight alternative, MobileNetV2. Our experimental results reveal that MobileNetV2 achieves an average F1-score less than a 5\% lower than ResNet50 (0.789 vs. 0.834), performing better than VGG16 with a footprint size nearly 40 times smaller. Moreover, to compare the models, we have created and made public the Western Mediterranean Wetland Birds dataset, consisting of 201.6 minutes and 5,795 audio excerpts of 20 endemic bird species of the Aiguamolls de l'Empord\`a Natural Park.Comment: 17 pages, 8 figures, 3 table

The importance of acoustic background modelling in CNN-based detection of the neotropical White-lored Spinetail (Aves, Passeriformes, Furnaridae)

Machine learning tools are widely used in support of bioacoustics studies, and there are numerous publications on the applicability of convolutional neural networks (CNNs) to the automated presence-absence detection of species. However, the relation between the merit of acoustic background modelling and the recognition performance needs to be better understood. In this study, we investigated the influence of acoustic background substance on the performance of the acoustic detector of the White-lored Spinetail (Synallaxis albilora). Two detector designs were evaluated: the 152-layer ResNet with transfer learning and a purposely created CNN. We experimented with acoustic background representations trained with season-specific (dry, wet, and all-season) data and without explicit modelling to evaluate its influence on the detection performance. The detector permits monitoring of the diel behaviour and breeding time of White-lored Spinetail solely based on the changes in the vocal activity patterns. We report an advantageous performance when background modelling is used, precisely when trained with all-season data. The highest classification accuracy (84.5%) was observed for the purposely created CNN model. Our findings contribute to an improved understanding of the importance of acoustic background modelling, which is essential for increasing the performance of CNN-based species detectors.This work was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brazil (CAPES) under Grant [CAPES-01]; Instituto Nacional de Ciência e Tecnologia em Áreas Úmidas (INAU/UFMT/CNPq); Centro de Pesquisa do Pantanal (CPP); and Brehm Funds for International Bird Conservation (BF), Germany

Acoustic monitoring of Amazonian wildlife in human-modified landscapes

Tropical forest covers just 12% of the planet’s land surface, but disproportionately host the planet’s biodiversity, including around two thirds of all terrestrial species. Amazonia retains the largest extent of remaining tropical forest globally, but just over 50% of all tropical forest loss since 2002 has been in the region. Deforestation and disturbance result in significant loss in forest biodiversity, but quantifying the exact nature of those changes can be complex. The Amazon represents a particularly challenging case in which to assess biodiversity change due to the spatiotemporal scales being assessed, because of the high proportion of rare species, and the challenging conditions for conducting biodiversity surveys in tropical forest. Ecoacoustics has been championed as a valuable tool to overcome the difficulties of monitoring in such conditions and at large spatio-temporal scales, but applied analytical methods often remain underdeveloped. In this this thesis I develop and use a range of ecoacoustic methods to help understand the impact of anthropogenic disturbance on Amazonian wildlife, using an extensive audio dataset collected from survey points spanning a degradation gradient in the Eastern Brazilian Amazon. In Chapter 2 I introduce a quick and simple method for the detection of rainfall, tested for efficacy globally and with an accompanying R package. In Chapter 3 I present a new approach to subsampling of acoustic data for manual assessment of avian biodiversity, finding that using a high number of short repeat samples can detect approximately 50% higher alpha diversity than more commonly used approaches. In Chapter 4 I assess the sensitivity and fidelity of two commonly used acoustic indices to biodiversity responses to forest disturbances, finding that measuring indices at narrower, ecologically appropriate time-frequency bins avoids problems with signal masking. In Chapter 5 I use a two-stage, random forest based method to build a multi-taxa classifier for the nocturnal avifaunal community in the study region, and use the classifier-derived data to reveal that the nocturnal bird community is largely robust to less intense forms of forest disturbance. Overall, in this thesis I demonstrate that ecoacoustics can be a highly effective method for inventorying and monitoring biodiversity in one of the most diverse and challenging regions on the planet