Multi-scale Transformer-based Network for Emotion Recognition from Multi Physiological Signals
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916370318360576 |
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| author | Vu, Tu Huynh, Van Thong Kim, Soo-Hyung |
| author_facet | Vu, Tu Huynh, Van Thong Kim, Soo-Hyung |
| contents | This paper presents an efficient Multi-scale Transformer-based approach for the task of Emotion recognition from Physiological data, which has gained widespread attention in the research community due to the vast amount of information that can be extracted from these signals using modern sensors and machine learning techniques. Our approach involves applying a Multi-modal technique combined with scaling data to establish the relationship between internal body signals and human emotions. Additionally, we utilize Transformer and Gaussian Transformation techniques to improve signal encoding effectiveness and overall performance. Our model achieves decent results on the CASE dataset of the EPiC competition, with an RMSE score of 1.45. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_00769 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Multi-scale Transformer-based Network for Emotion Recognition from Multi Physiological Signals Vu, Tu Huynh, Van Thong Kim, Soo-Hyung Signal Processing Artificial Intelligence Machine Learning This paper presents an efficient Multi-scale Transformer-based approach for the task of Emotion recognition from Physiological data, which has gained widespread attention in the research community due to the vast amount of information that can be extracted from these signals using modern sensors and machine learning techniques. Our approach involves applying a Multi-modal technique combined with scaling data to establish the relationship between internal body signals and human emotions. Additionally, we utilize Transformer and Gaussian Transformation techniques to improve signal encoding effectiveness and overall performance. Our model achieves decent results on the CASE dataset of the EPiC competition, with an RMSE score of 1.45. |
| title | Multi-scale Transformer-based Network for Emotion Recognition from Multi Physiological Signals |
| topic | Signal Processing Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2305.00769 |