TemporalAugmenter: An Ensemble Recurrent Based Deep Learning Approach for Signal Classification
This is an incremental improvement for signal classification tasks in domains like industrial, medical, and human-computer interaction, potentially reducing computational costs.
The paper tackles the problem of capturing temporal dependencies in signal classification by proposing TemporalAugmenter, an ensemble recurrent deep learning approach that integrates two variations of RNNs to enhance temporal information extraction, resulting in reduced preprocessing and energy requirements for green AI.
Ensemble modeling has been widely used to solve complex problems as it helps to improve overall performance and generalization. In this paper, we propose a novel TemporalAugmenter approach based on ensemble modeling for augmenting the temporal information capturing for long-term and short-term dependencies in data integration of two variations of recurrent neural networks in two learning streams to obtain the maximum possible temporal extraction. Thus, the proposed model augments the extraction of temporal dependencies. In addition, the proposed approach reduces the preprocessing and prior stages of feature extraction, which reduces the required energy to process the models built upon the proposed TemporalAugmenter approach, contributing towards green AI. Moreover, the proposed model can be simply integrated into various domains including industrial, medical, and human-computer interaction applications. Our proposed approach empirically evaluated the speech emotion recognition, electrocardiogram signal, and signal quality examination tasks as three different signals with varying complexity and different temporal dependency features.