Ensemble of convolutional neural networks to improve animal audio classification

Abstract In this work, we present an ensemble for automated audio classification that fuses different types of features extracted from audio files. These features are evaluated, compared, and fused with the goal of producing better classification accuracy than other state-of-the-art approaches without ad hoc parameter optimization. We present an ensemble of classifiers that performs competitively on different types of animal audio datasets using the same set of classifiers and parameter settings. To produce this general-purpose ensemble, we ran a large number of experiments that fine-tuned pretrained convolutional neural networks (CNNs) for different audio classification tasks (bird, bat, and whale audio datasets). Six different CNNs were tested, compared, and combined. Moreover, a further CNN, trained from scratch, was tested and combined with the fine-tuned CNNs. To the best of our knowledge, this is the largest study on CNNs in animal audio classification. Our results show that several CNNs can be fine-tuned and fused for robust and generalizable audio classification. Finally, the ensemble of CNNs is combined with handcrafted texture descriptors obtained from spectrograms for further improvement of performance. The MATLAB code used in our experiments will be provided to other researchers for future comparisons at https://github.com/LorisNanni

Species misidentification in ecological studies : incidence and importance from the ecologists’ point of view

Natural scientists study a wide variety of species, but whether they have identified all studied samples correctly to species is rarely evaluated. Species misidentification in empirical research can cause significant losses of money, information, and time, and contribute to false results. Thus, I study the abundance of species misidentification and ecologists’ perceptions of such mistakes through a web survey targeting researchers from scientific institutes around the globe (including universities, research societies and museums) who completed their doctoral degree in any ecology-related field of science. I received 117 responses with either work or educational background from 30 countries. I found that species misidentification widely existed in respondents’ research: almost 70% of the respondents noticed species misidentification in their own research, while the estimated proportion of existing studies with species misidentification was 34% (95% CI: 28% - 40%). Although misidentification was mainly found during specimen collection, specimen handling and data analysis, misidentifications in reporting stages (writing, revision and after publishing) could persist until publication. Moreover, according to respondents, reviewers seldom comment about species identification methods or their accuracy, which may affect respondents’ (both leading and not leading a research team) low reporting frequency about the possibility of misidentification. Expert checking, training students, and DNA barcoding are the most prevalent approaches to ensure identification accuracy among respondents. My results imply that species misidentification might be widespread in existing ecological research. Although the problem of species misidentification is widely recognized, such an issue seldom be appropriately handled by respondents. To increase the accuracy of species identification and maintain academic integrity, I suggest that researchers need to focus more on the study species (e.g., sampling process, identification method, and accuracy) when writing and reviewing papers. Furthermore, I appeal for guidelines about reporting species identification methods and their accuracy in papers, as well as research on education about identification skills in universities, as these two topics may constrain the precision of species identification

Bird and whale species identification using sound images

Image identification of animals is mostly centred on identifying them based on their appearance, but there are other ways images can be used to identify animals, including by representing the sounds they make with images. In this study, the authors present a novel and effective approach for automated identification of birds and whales using some of the best texture descriptors in the computer vision literature. The visual features of sounds are built starting from the audio file and are taken from images constructed from different spectrograms and from harmonic and percussion images. These images are divided into sub‐windows from which sets of texture descriptors are extracted. The experiments reported in this study using a dataset of Bird vocalisations targeted for species recognition and a dataset of right whale calls targeted for whale detection (as well as three well‐known benchmarks for music genre classification) demonstrate that the fusion of different texture features enhances performance. The experiments also demonstrate that the fusion of different texture features with audio features is not only comparable with existing audio signal approaches but also statistically improves some of the stand‐alone audio features. The code for the experiments will be publicly available at https://www.dropbox.com/s/bguw035yrqz0pwp/ElencoCode.docx?dl=0