A Efficient Multimodal Framework for Large Scale Emotion Recognition by Fusing Music and Electrodermal Activity Signals

TL;DR

This paper introduces an efficient multimodal framework that combines electrodermal activity signals and music features for large-scale emotion recognition, utilizing a novel neural network architecture and signal decomposition techniques.

Contribution

The work presents RTCAN-1D, an end-to-end residual network with attention mechanisms that fuses EDA and music features, improving emotion recognition accuracy on large datasets.

Findings

Outperforms existing state-of-the-art models on three datasets.

Effectively decomposes EDA signals into phasic and tonic components.

Provides a reliable and scalable solution for emotion recognition.

Abstract

Considerable attention has been paid for physiological signal-based emotion recognition in field of affective computing. For the reliability and user friendly acquisition, Electrodermal Activity (EDA) has great advantage in practical applications. However, the EDA-based emotion recognition with hundreds of subjects still lacks effective solution. In this paper, our work makes an attempt to fuse the subject individual EDA features and the external evoked music features. And we propose an end-to-end multimodal framework, the 1-dimensional residual temporal and channel attention network (RTCAN-1D). For EDA features, the novel convex optimization-based EDA (CvxEDA) method is applied to decompose EDA signals into pahsic and tonic signals for mining the dynamic and steady features. The channel-temporal attention mechanism for EDA-based emotion recognition is firstly involved to improve the temporal- and channel-wise representation. For music features, we process the music signal with the open source toolkit openSMILE to obtain external feature vectors. The individual emotion features from EDA signals and external emotion benchmarks from music are fused in the classifing layers. We have conducted systematic comparisons on three multimodal datasets (PMEmo, DEAP, AMIGOS) for 2-classes valance/arousal emotion recognition. Our proposed RTCAN-1D outperforms the existing state-of-the-art models, which also validate that our work provides an reliable and efficient solution for large scale emotion recognition. Our code has been released at https://github.com/guanghaoyin/RTCAN-1D.