ArticleSensors (Basel, Switzerland)2021
Deep-Learning-Based Multimodal Emotion Classification for Music Videos.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- ICT training with wearable devices to enhance emotional communication and musicality in vocal education.Scientific reports · 2026Article
- [Research on bimodal emotion recognition algorithm based on multi-branch bidirectional multi-scale time perception].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Emotion-RGC net: A novel approach for emotion recognition in social media using RoBERTa and Graph Neural Networks.PloS one · 2025Article
- Article
- A dataset for multimodal music information retrieval of Sotho-Tswana musical videos.Data in brief · 2024Article
- Musicians and researchers: two creative professions striving to improve heart health through music.Frontiers in cardiovascular medicine · 2024Review
- Multimodal robotic music performance art based on GRU-GoogLeNet model fusing audiovisual perception.Frontiers in neurorobotics · 2023Article
- The influence of music teaching appreciation on the mental health of college students based on multimedia data analysis.PeerJ. Computer science · 2023Article
- MERP: A Music Dataset with Emotion Ratings and Raters' Profile Information.Sensors (Basel, Switzerland) · 2022Article
- Research on Music Style Classification Based on Deep Learning.Computational and mathematical methods in medicine · 2022Article
- Music video emotion classification using slow-fast audio-video network and unsupervised feature representation.Scientific reports · 2021Article
- Subgroup Preference Neural Network.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
3 authors.
Funding
Abstract
Music videos contain a great deal of visual and acoustic information. Each information source within a music video influences the emotions conveyed through the audio and video, suggesting that only a multimodal approach is capable of achieving efficient affective computing. This paper presents an affective computing system that relies on music, video, and facial expression cues, making it useful for emotional analysis. We applied the audio-video information exchange and boosting methods to regularize the training process and reduced the computational costs by using a separable convolution strategy. In sum, our empirical findings are as follows: (1) Multimodal representations efficiently capture all acoustic and visual emotional clues included in each music video, (2) the computational cost of each neural network is significantly reduced by factorizing the standard 2D/3D convolution into separate channels and spatiotemporal interactions, and (3) information-sharing methods incorporated into multimodal representations are helpful in guiding individual information flow and boosting overall performance. We tested our findings across several unimodal and multimodal networks against various evaluation metrics and visual analyzers. Our best classifier attained 74% accuracy, an f1-score of 0.73, and an area under the curve score of 0.926.
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