Millennial-scale changes in sea surface temperatures and intermediate water circulation in the northwest Pacific during the past 20,000 years

During the end of the late Pleistocene, the large-scale shift from the last glacial state to the recent interglacial state took place and was accompanied by millennial-scale climate fluctuations. However, detailed paleoceanographic reconstructions of the subarctic North Pacific are scarce and an incomplete picture of short-term climate fluctuations of the late Pleistocene to Holocene remains so far. The principal aim of this thesis was the reconstruction of the poorly studied (millennial-scale) climate variability of the subarctic northwest Pacific by means of detailed paleoceanographic investigations of past dynamics in sea surface temperatures, sea-ice variability and intermediate water ventilation characteristics of the northwest Pacific realm during the past 20,000 years. Altogether, the results of this thesis point to rapid changes in climate and oceanography of the subarctic North Pacific due to the sensitivity to millennial-scale climate fluctuations of the last deglaciation

Utilizing Domain Knowledge in End-to-End Audio Processing

End-to-end neural network based approaches to audio modelling are generally outperformed by models trained on high-level data representations. In this paper we present preliminary work that shows the feasibility of training the first layers of a deep convolutional neural network (CNN) model to learn the commonly-used log-scaled mel-spectrogram transformation. Secondly, we demonstrate that upon initializing the first layers of an end-to-end CNN classifier with the learned transformation, convergence and performance on the ESC-50 environmental sound classification dataset are similar to a CNN-based model trained on the highly pre-processed log-scaled mel-spectrogram features.Comment: Accepted at the ML4Audio workshop at the NIPS 201