Professionals of all domains of expertise expect to take part in the benefits of the machine
learning (ML) revolution, but realisation is often slowed down by lack of familiarity
with ML concepts and tools, as well as low availability of annotated data for supervised
methods. Inspired by the problem of assessing the impact of human-generated activity on
marine ecosystems through passive acoustic monitoring [1], we are developing Seadash,
an interactive tool for event detection and classification in multivariate time series.

Full article: Poster Paper 7726