LifeCLEF 2016: Multimedia Life Species Identification Challenges

International audienceUsing multimedia identification tools is considered as one of the most promising solutions to help bridge the taxonomic gap and build accurate knowledge of the identity, the geographic distribution and the evolution of living species. Large and structured communities of nature observers (e.g., iSpot, Xeno-canto, Tela Botanica, etc.) as well as big monitoring equipment have actually started to produce outstanding collections of multimedia records. Unfortunately, the performance of the state-of-the-art analysis techniques on such data is still not well understood and is far from reaching real world requirements. The LifeCLEF lab proposes to evaluate these challenges around 3 tasks related to multimedia information retrieval and fine-grained classification problems in 3 domains. Each task is based on large volumes of real-world data and the measured challenges are defined in collaboration with biologists and environmental stakeholders to reflect realistic usage scenarios. For each task, we report the methodology, the data sets as well as the results and the main outcom

Sound event detection in the DCASE 2017 Challenge

International audienceEach edition of the challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) contained several tasks involving sound event detection in different setups. DCASE 2017 presented participants with three such tasks, each having specific datasets and detection requirements: Task 2, in which target sound events were very rare in both training and testing data, Task 3 having overlapping events annotated in real-life audio, and Task 4, in which only weakly-labeled data was available for training. In this paper, we present the three tasks, including the datasets and baseline systems, and analyze the challenge entries for each task. We observe the popularity of methods using deep neural networks, and the still widely used mel frequency based representations, with only few approaches standing out as radically different. Analysis of the systems behavior reveals that task-specific optimization has a big role in producing good performance; however, often this optimization closely follows the ranking metric, and its maximization/minimization does not result in universally good performance. We also introduce the calculation of confidence intervals based on a jackknife resampling procedure, to perform statistical analysis of the challenge results. The analysis indicates that while the 95% confidence intervals for many systems overlap, there are significant difference in performance between the top systems and the baseline for all tasks

Marine biodiversity assessments using aquatic internet of things

While Ubiquitous Computing remains vastly applied in urban environments, it is still scarce in oceanic environments. Current equipment used for biodiversity assessments remain at a high cost, being still inaccessible to wider audiences. More accessible IoT (Internet of Things) solutions need to be implemented to tackle these issues and provide alternatives to facilitate data collection in-the-wild. While the ocean remains a very harsh environment to apply such devices, it is still providing the opportunity to further explore the biodiversity, being that current marine taxa is estimated to be 200k, while this number can be actually in millions. The main goal of this thesis is to provide an apparatus and architecture for aerial marine biodiversity assessments, based on low-cost MCUs (Microcontroller unit) and microcomputers. In addition, the apparatus will provide a proof of concept for collecting and classifying the collected media. The thesis will also explore and contribute to the latest IoT and machine learning techniques (e.g. Python, TensorFlow) when applied to ocean settings. The final product of the thesis will enhance the state of the art in technologies applied to marine biology assessments.A computação ubíqua é imensamente utilizada em ambientes urbanos, mas ainda é escassa em ambientes oceânicos. Os equipamentos atuais utilizados para o estudo de biodiversidade são de custo alto, e geralmente inacessíveis para o público geral. Uma solução IoT mais acessível necessita de ser implementada para combater estes problemas e fornecer alternativas para facilitar a recolha de dados na natureza. Embora o oceano seja um ambiente severo para aplicar estes dispositivos, este fornece mais oportunidades para explorar a biodiversidade, sendo que a taxa de marinha atual é estimada ser 200 mil, mas este número pode estar nos milhões. O objetivo principal desta tese é fornecer um aparelho e uma arquitetura para estudos aéreos de biodiversidade marinha, baseado em microcontroladores low-cost e microcomputadores. Adi cionalmente, este aparelho irá fornecer uma prova de conceito para coletar e classificar a mídia coletada. A tese irá também explorar e contribuir para as técnicas mais recentes de machine learn ing (e.g. Python, TensorFlow) quando aplicadas num cenário de oceano. O produto final desta tese vai elevar o estado da arte em tecnologias aplicadas a estudos de biologia marinha