  In this paper, we propose Emotionally paired Music and Image Dataset (EMID),
a novel dataset designed for the emotional matching of music and images, to
facilitate auditory-visual cross-modal tasks such as generation and retrieval.
Unlike existing approaches that primarily focus on semantic correlations or
roughly divided emotional relations, EMID emphasizes the significance of
emotional consistency between music and images using an advanced 13-dimension
emotional model. By incorporating emotional alignment into the dataset, it aims
to establish pairs that closely align with human perceptual understanding,
thereby raising the performance of auditory-visual cross-modal tasks. We also
design a supplemental module named EMI-Adapter to optimize existing cross-modal
alignment methods. To validate the effectiveness of the EMID, we conduct a
psychological experiment, which has demonstrated that considering the emotional
relationship between the two modalities effectively improves the accuracy of
matching in abstract perspective. This research lays the foundation for future
cross-modal research in domains such as psychotherapy and contributes to
advancing the understanding and utilization of emotions in cross-modal
alignment. The EMID dataset is available at https://github.com/ecnu-aigc/EMID.
