In this work, we propose a light-weight, multimodal framework for environmental audio tagging. The audio branch of the framework is a convolutional and ...
A light-weight multimodal framework for improved environmental audio tagging. J Li, Y Wang, J Szurley, F Metze, S Das. 2018 IEEE International Conference on ...
In this work, we propose a light-weight, multimodal framework for environmental audio tagging. The audio branch of the framework is a convolutional and ...
In this work, we propose a light-weight, multimodal framework for environmental audio tagging. The audio branch of the framework is a convolutional and ...
The aim of this paper is to categorize movies into genres using the previews. Our study attempts to combine audio, visual and text features to classify a ...
Oct 25, 2024 · A Light-Weight Multimodal Framework for Improved Environmental Audio Tagging. ICASSP 2018: 6832-6836. [c10]. view. electronic edition via DOI ...
This paper proposes a small-footprint multiple instance learning (MIL) framework ... A Light-Weight Multimodal Framework for Improved Environmental Audio Tagging.
A light-weight multimodal framework for improved environmental audio tagging. In Proc. ICASSP, Calgary, BC; Canada, April 2018. IEEE. 17: Odette Scharenborg ...
Audio and Visual. A Light-Weight Multimodal Framework for Improved Environmental Audio Tagging, ICASSP 2018. Large Scale Audiovisual Learning of Sounds with ...
Juncheng Li, Yun Wang, Joseph Szurley, Florian Metze, and Samarjit Das, "A light-weight multimodal framework for improved environmental audio tagging", in ...