# TemporalAugmenter: An Ensemble Recurrent Based Deep Learning Approach for Signal Classification

arXiv: https://arxiv.org/abs/2401.06970 · HTML: https://mlsanity.com/paper/2401.06970/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 74 references that are in the map.
- First posted on arXiv 13 Jan 2024.

## Builds on (most cited first, 19 of 19)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [LSTM: A Search Space Odyssey](https://mlsanity.com/paper/1503.04069/) (2015)
- [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021)
- [Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks](https://mlsanity.com/paper/1701.05923/) (2017)
- [Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization](https://mlsanity.com/paper/2110.10832/) (2021)
- [ECG Heartbeat Classification: A Deep Transferable Representation](https://mlsanity.com/paper/1805.00794/) (2018)
- [Minimal Gated Unit for Recurrent Neural Networks](https://mlsanity.com/paper/1603.09420/) (2016)
- [Diversity and Generalization in Neural Network Ensembles](https://mlsanity.com/paper/2110.13786/) (2021)
- [Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification](https://mlsanity.com/paper/1812.07683/) (2018)
- [GAN Ensemble for Anomaly Detection](https://mlsanity.com/paper/2012.07988/) (2020)
- [Insights into LSTM Fully Convolutional Networks for Time Series Classification](https://mlsanity.com/paper/1902.10756/) (2019)
- [Speech Emotion Recognition using Supervised Deep Recurrent System for Mental Health Monitoring](https://mlsanity.com/paper/2208.12812/) (2022)
- [Ensemble One-dimensional Convolution Neural Networks for Skeleton-based Action Recognition](https://mlsanity.com/paper/1801.02475/) (2018)
- [Smart City Transportation: Deep Learning Ensemble Approach for Traffic Accident Detection](https://mlsanity.com/paper/2310.10038/) (2023)
- [Improving Automatic Emotion Recognition from speech using Rhythm and Temporal feature](https://mlsanity.com/paper/1303.1761/) (2013)
- [Gated Recurrent Networks for Seizure Detection](https://mlsanity.com/paper/1801.02471/) (2018)
- [Enhancing Certifiable Robustness via a Deep Model Ensemble](https://mlsanity.com/paper/1910.14655/) (2019)
- [Machine Learning at Scale](https://mlsanity.com/paper/1402.6076/) (2014)
- [Biologically inspired speech emotion recognition](https://mlsanity.com/paper/2111.08112/) (2021)

## Abstract (arXiv)

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.