Matthew Sharpe

Cameron Trotter, Georgia Atkinson, Matt Sharpe et al.

We introduce the Northumberland Dolphin Dataset 2020 (NDD20), a challenging image dataset annotated for both coarse and fine-grained instance segmentation and categorisation. This dataset, the first release of the NDD, was created in response to the rapid expansion of computer vision into conservation research and the production of field-deployable systems suited to extreme environmental conditions -- an area with few open source datasets. NDD20 contains a large collection of above and below water images of two different dolphin species for traditional coarse and fine-grained segmentation. All data contained in NDD20 was obtained via manual collection in the North Sea around the Northumberland coastline, UK. We present experimentation using standard deep learning network architecture trained using NDD20 and report baselines results.

Matthew Sharpe

Podcasts are traditionally shared through RSS feeds. As well as pointing to the audio files, RSS gives a creator a way of providing metadata about the podcast shows and episodes. We investigate how certain metadata fields associated with podcasts are currently being used and comment on their applicability to recommendations. Specifically, we find that many creators are not using the itunes:type field in the expected fashion, and that using this field for recommendations might not lead to an optimal user experience. We perform similar explorations for the season number and the category associated with a podcast, and also find that the fields aren't being used in the expected fashion. Finally, we examine the notion that a single podcast show is the same as a single RSS feed. This also turns out to not be strictly true in all cases. In short, the metadata associated with many podcasts isn't always reflective of the show and should be used with caution.

Cameron Trotter, Georgia Atkinson, Matthew Sharpe et al.

Methods for cetacean research include photo-identification (photo-id) and passive acoustic monitoring (PAM) which generate thousands of images per expedition that are currently hand categorised by researchers into the individual dolphins sighted. With the vast amount of data obtained it is crucially important to develop a system that is able to categorise this quickly. The Northumberland Dolphin Dataset (NDD) is an on-going novel dataset project made up of above and below water images of, and spectrograms of whistles from, white-beaked dolphins. These are produced by photo-id and PAM data collection methods applied off the coast of Northumberland, UK. This dataset will aid in building cetacean identification models, reducing the number of human-hours required to categorise images. Example use cases and areas identified for speed up are examined.