no code implementations • 18 Nov 2022 • Wim Boes, Hugo Van hamme
More specifically, visual features focusing on semantics appear appropriate in the context of automated audio captioning, while for sound event detection, time information seems to be more important.
no code implementations • 18 Oct 2022 • Wim Boes, Hugo Van hamme
This is significantly better than the performance obtained by the baseline model (0. 527), which can effectively be attributed to the changes that were applied to the pooling operations of the network.
no code implementations • 18 Oct 2022 • Wim Boes, Hugo Van hamme
With regard to the accuracy measure, our best model achieved a score of 77. 1\% on the validation data, which is about the same as the performance obtained by the baseline system (77. 0\%).
no code implementations • 26 Sep 2022 • Wim Boes, Hugo Van hamme
Large-scale sound recognition data sets typically consist of acoustic recordings obtained from multimedia libraries.
no code implementations • 26 Sep 2022 • Wim Boes, Hugo Van hamme
Many state-of-the-art systems for audio tagging and sound event detection employ convolutional recurrent neural architectures.
no code implementations • 16 Jun 2021 • Wim Boes, Robbe Van Rompaey, Lyan Verwimp, Joris Pelemans, Hugo Van hamme, Patrick Wambacq
We inspect the long-term learning ability of Long Short-Term Memory language models (LSTM LMs) by evaluating a contextual extension based on the Continuous Bag-of-Words (CBOW) model for both sentence- and discourse-level LSTM LMs and by analyzing its performance.
no code implementations • 9 Jun 2021 • Wim Boes, Hugo Van hamme
We study the merit of transfer learning for two sound recognition problems, i. e., audio tagging and sound event detection.
no code implementations • 2 Dec 2019 • Wim Boes, Hugo Van hamme
We tackle the task of environmental event classification by drawing inspiration from the transformer neural network architecture used in machine translation.