mlsanityData to 7 Oct 2026

Towards Audio to Scene Image Synthesis using Generative Adversarial Network

arXiv 1808.04108 ↗First posted 13 Aug 2018cs.CL cs.CV cs.SDcited by 23

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Abstract (arXiv)

Humans can imagine a scene from a sound. We want machines to do so by using conditional generative adversarial networks (GANs). By applying the techniques including spectral norm, projection discriminator and auxiliary classifier, compared with naive conditional GAN, the model can generate images with better quality in terms of both subjective and objective evaluations. Almost three-fourth of people agree that our model have the ability to generate images related to sounds. By inputting different volumes of the same sound, our model output different scales of changes based on the volumes, showing that our model truly knows the relationship between sounds and images to some extent.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Aug 2018.

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Citing papers per year: 2021: 5; 2022: 4; 2023: 8; 2024: 1; 2025: 3; 2026: 2.

23 papers.

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  1. 2023Diverse and Aligned Audio-to-Video Generation via Text-to-Video Model Adaptationcited by 68
  2. 2021Multimodal Image Synthesis and Editing: The Generative AI Eracited by 52
  3. 2023Sound to Visual Scene Generation by Audio-to-Visual Latent Alignmentcited by 41
  4. 2022RepMix: Representation Mixing for Robust Attribution of Synthesized Imagescited by 39
  5. 2021Sound-Guided Semantic Image Manipulationcited by 35
  6. 2022A Review on Methods and Applications in Multimodal Deep Learningcited by 29
  7. 2023AudioToken: Adaptation of Text-Conditioned Diffusion Models for Audio-to-Image Generationcited by 22
  8. 2022Robust Sound-Guided Image Manipulationcited by 7
  9. 2021Collaborative Learning to Generate Audio-Video Jointlycited by 6
  10. 2021Visual-Tactile Cross-Modal Data Generation using Residue-Fusion GAN with Feature-Matching and Perceptual Lossescited by 6
  11. 2024Sound2Vision: Generating Diverse Visuals from Audio through Cross-Modal Latent Alignmentcited by 5
  12. 2023Generating Realistic Images from In-the-wild Soundscited by 4
11 more
  1. 2021The Boombox: Visual Reconstruction from Acoustic Vibrationscited by 3
  2. 2025MACS: Multi-source Audio-to-image Generation with Contextual Significance and Semantic Alignmentcited by 2
  3. 2022The applicability of transperceptual and deep learning approaches to the study and mimicry of complex cartilaginous tissuescited by 1
  4. 2023Exploring Efficient-Tuned Learning Audio Representation Method from BriVLcited by 1
  5. 2023UniBriVL: Robust Universal Representation and Generation of Audio Driven Diffusion Modelscited by 1
  6. 2025Designing ReLU Generative Networks to Enumerate Trees with a Given Tree Edit Distancecited by 1
  7. 2023Towards Unseen Triples: Effective Text-Image-joint Learning for Scene Graph Generationcited by 0
  8. 2023Deep Video Inpainting Guided by Audio-Visual Self-Supervisioncited by 0
  9. 2025SeeingSounds: Learning Audio-to-Visual Alignment via Textcited by 0
  10. 2026ReLU Networks for Exact Generation of Similar Graphscited by 0
  11. 2026Face and Voice Cross-modal Association with Learning Convex Feature Embeddingcited by 0