Reproduce “Emotion-Guided Image to Music Generation” on OpenResearch
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About this paper
Souraja Kundu, Saket Singh, Yuji Iwahori
Researchers from IIT Guwahati, Samsung R&D, and Chubu University developed an emotion-guided image-to-music generation system. Their CNN-Transformer model, utilizing a novel Valence-Arousal (VA) loss function, produces musically and emotionally consistent MIDI sequences from images, outperforming a baseline LSTM model with a Music_Quality_Loss of 0.7061 compared to 1.9157 for the baseline.
Key insights
- Direct integration of a Valence-Arousal (VA) loss function offers a more straightforward and effective mechanism for emotional alignment in image-to-music generation compared to complex contrastive learning.
- A CNN-Transformer architecture significantly outperforms CNN-LSTM models for this task, leveraging multi-headed attention to capture complex emotional context and long-range dependencies in music.
- An optimal architectural balance, specifically 3 Transformer encoder and decoder blocks, is crucial for capturing intricate high-level emotional features in generated music without overfitting to a limited dataset.
Summary by alphaXiv.
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