Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production

Vocal expression of emotions has been observed across species and could provide a non-invasive and reliable means to assess animal emotions. We investigated if pig vocal indicators of emotions revealed in previous studies are valid across call types and contexts, and could potentially be used to develop an automated emotion monitoring tool. We performed an analysis of an extensive and unique dataset of low (LF) and high frequency (HF) calls emitted by pigs across numerous commercial contexts from birth to slaughter (7414 calls from 411 pigs). Our results revealed that the valence attributed to the contexts of production (positive versus negative) affected all investigated parameters in both LF and HF. Similarly, the context category affected all parameters. We then tested two different automated methods for call classification; a neural network revealed much higher classification accuracy compared to a permuted discriminant function analysis (pDFA), both for the valence (neural network: 91.5%; pDFA analysis weighted average across LF and HF (cross-classified): 61.7% with a chance level at 50.5%) and context (neural network: 81.5%; pDFA analysis weighted average across LF and HF (cross-classified): 19.4% with a chance level at 14.3%). These results suggest that an automated recognition system can be developed to monitor pig welfare on-farm.publishedVersio

Deep neural networks for automated detection of marine mammal species

Authors thank the Bureau of Ocean Energy Management for the funding of MARU deployments, Excelerate Energy Inc. for the funding of Autobuoy deployment, and Michael J. Weise of the US Office of Naval Research for support (N000141712867).Deep neural networks have advanced the field of detection and classification and allowed for effective identification of signals in challenging data sets. Numerous time-critical conservation needs may benefit from these methods. We developed and empirically studied a variety of deep neural networks to detect the vocalizations of endangered North Atlantic right whales (Eubalaena glacialis). We compared the performance of these deep architectures to that of traditional detection algorithms for the primary vocalization produced by this species, the upcall. We show that deep-learning architectures are capable of producing false-positive rates that are orders of magnitude lower than alternative algorithms while substantially increasing the ability to detect calls. We demonstrate that a deep neural network trained with recordings from a single geographic region recorded over a span of days is capable of generalizing well to data from multiple years and across the species’ range, and that the low false positives make the output of the algorithm amenable to quality control for verification. The deep neural networks we developed are relatively easy to implement with existing software, and may provide new insights applicable to the conservation of endangered species.Publisher PDFPeer reviewe

Underwater computing systems and astronomy–multi-disciplinary research potential and benefits

Abstract: The Ocean plays an important role in hosting investigations in underwater astronomy and enabling the realization of new research prospects. This paper discusses synergistic prospects of the blue economy from the perspective of underwater astronomy and scientific inquiry, technological and economic development. The presented research investigates how the synergy enhances computing applications. The paper presents the overloaded application paradigm that explores the ability of underwater telescope networks to accommodate additional applications. The investigated metrics for computing applications are the accessible computational resources, and power usage effectiveness (PUE) that is investigated in a scenario where onshore computing stations used in underwater astronomy observations are integrated with existing terrestrial data centers. This is necessary as onshore computing stations benefit from free cooling being located near natural maritime resources. The performance evaluation investigates how the proposed synergy and the emerging crowd–sourcing can enhance the observation resolution for underwater astronomy observations. Investigation shows that the synergy enhances accessible computational resources, PUE and observation resolution by an average of 48.8%, 1.6% and 41.3%, respectively

Automatic Classification of Ultrasonic Harbor Porpoise Clicks in a Varying Noise Environment

This study compares three approaches in the design of an autonomous machine listening agent that predicts harbor porpoise ultrasonic echolocation clicks in diverse noise environments. Considering the temporal variations of noisy coastal ocean soundscapes which the harbor porpoises inhabit, we propose a leave-one-day-out (LODO) cross-validation strategy in the training of a random forest classifier that successfully addressed the covariate shift present in our time-series data. To evaluate the efficacy of our approach to capture signals in this noisy environment, we compare three preprocessing approaches and two deep learning architectures on our harbor porpoise click data. We find that feature extraction strategies of mel frequency cepstral coefficients (MFCC) and short time Fourier transform (STFT) outperformed our novel approach, the heterodyned-Teager-Kaiser Energy Operator (TKEO), which shifts down the ultrasonic signal to a lower frequency in the time domain. Building on these results, we seek to improve the robustness of our porpoise click classifier for a real-world environment by implementing a second-stage stacked random forest ensemble on combinations of subsets of 42 deep learning base models that were trained from the folds of our LODO cross-validation and the three preprocessing approaches that were explored in this study. Our results demonstrate that experiments using the LODO cross-validation strategy reported a difference between the average fold accuracy and a held-out test accuracy of 6%, while training without cross-validation and the equal k-fold cross-validation reported a 28.7% and 30.4% difference, respectively. From the three preprocessing approaches we implement, the models trained on MFCC produced the highest accuracy of 95.6% on the held-out test set while those trained on STFT and heterodyned-TKEO produced accuracies on the same held-out test set of 88.7% and 85.0%, respectively. Results from our stacked random forest show the greatest improvement in accuracy of 5.6% in the heterodyned-TKEO models while the STFT and MFCC models reported 4.5% and 1.9% improvements in accuracy, respectively. Highly varying noise environments are common across coastal areas inhabited by harbor porpoises. This study, with our proposed ensemble of different feature and model architectures, emphasizes the need to overcome such shifts in noise to design a robust porpoise click classifier that is ready for real-time deployment and able to generalize to all real-world conditions