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arXiv · 2410.22299

Emotion-Guided Image to Music Generation

Souraja Kundu·Saket Singh·Yuji Iwahori
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Abstract

Generating music from images can enhance various applications, including background music for photo slideshows, social media experiences, and video creation. This paper presents an emotion-guided image-to-music generation framework that leverages the Valence-Arousal (VA) emotional space to produce music that aligns with the emotional tone of a given image. Unlike previous models that rely on contrastive learning for emotional consistency, the proposed approach directly integrates a VA loss function to enable accurate emotional alignment. The model employs a CNN-Transformer architecture, featuring pre-trained CNN image feature extractors and three Transformer encoders to capture complex, high-level emotional features from MIDI music. Three Transformer decoders refine these features to generate musically and emotionally consistent MIDI sequences. Experimental results on a newly curated emotionally paired image-MIDI dataset demonstrate the proposed model's superior performance across metrics such as Polyphony Rate, Pitch Entropy, Groove Consistency, and loss convergence.

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BibTeXRIS

Souraja Kundu, Saket Singh, Yuji Iwahori. 2024-10-29. Emotion-Guided Image to Music Generation. https://arxiv.org/abs/2410.22299

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