Abstract: Music is an important means of emotional expression for people,serving as a powerful tool for conveying feelings.Music emotion transformation technology allows for the conversion of original music into music with a target emotion,thereby mee-ting users’ demands for diverse emotional music and significantly improving creative efficiency.The existing music emotion transformation technologies achieve end-to-end emotion transformation by constructing sophisticated deep learning models.However,in current methods,the correspondence between the emotional vector representing music and the actual musical features is insufficient,resulting in a lack of interpretability in the intermediate network layers,which limits the accuracy of emotion transformation to a certain extent and may contribute to the problem of gradient vanishing.To address these issues,a new music emotion transformation model based on the CVAE-WGAN(Conditional Variational Autoencoder Wasserstein Generative Adversarial Network) architecture is proposed.This model uses WGAN-GP Network to replace the traditional GAN module and introduces Wasserstein distance and gradient penalty mechanism,which effectively avoids mode collapse and gradient vanishing,thereby further enhancing the stability of training and the quality of generated music outputs.Meanwhile,in order to address the lack of interpretability in the intermediate process of the generative model,64 kinds of intermediate perceptual features with clear interpretability are introduced,covering aspects such as music melody,harmony,rhythm,dynamics,timbre,expressiveness and form.These features are incorporated into the model as latent space variables to ensure that each dimension of the latent space corresponds to a specific and meaningful musical feature.In addition,a Gaussian mixture model is employed in place of the single Gaussian model tradi-tionally used in the variational autoencoder to capture and represent the nuanced distribution of musical features across different emotional categories.The experimental results show that this model performs excellently in transformations among six distinct emotions－happiness,sadness,tenderness,anger,fear,and surprise.Moreover,the proposed model outperforms comparative mo-dels in terms of emotional accuracy,reconstruction error,generation coherence,and generation diversity.

Key words: Emotion transformation in music, CVAE-WGAN, Swin transformer, Intermediate perceptual features, Gaussian mixture model