ENTERPRISE AI ANALYSIS
Hearable Image: On-Device Image-Driven Sound Effect Generation for Hearing What You See
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Executive Impact: At a Glance
This paper presents a novel framework for on-device image-driven sound effect generation, addressing computational constraints and stability issues in mobile environments. It introduces an Audio Feature Dictionary and Audio-Image Matching Pipeline for stable, predefined sound effect generation. A Multi-Category Generation and Generation Flow Map enable diverse sound effects, while lightweight model training (low computational cost, 4-step latent diffusion) ensures smartphone implementation feasibility. Experiments demonstrate competitive generation quality and audio-image matching performance compared to larger models, making real-time on-device inference viable.
99.86M
Model Parameters
0.45s
Real-Time Factor
4
Inference Steps
Deep Analysis & Enterprise Applications
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Computational Efficiency
Generation Stability
Output Diversity & Control
The framework achieves significant computational efficiency suitable for mobile devices, contrasting with the high demands of traditional diffusion models.
By using a predefined Audio Feature Dictionary and an Audio-Image Matching Pipeline, the system ensures stable and predictable sound effect generation, avoiding the erratic outputs of direct image-to-audio models.
Multi-Category Generation and a Generation Flow Map allow for diverse sound effect outputs from a single image and provide fine-grained control over audio characteristics like loudness progression.
99.86M
Total Parameters for On-Device Deployment
The proposed framework is optimized for mobile devices, enabling high-quality sound generation without requiring cloud infrastructure.
On-Device Sound Effect Generation Flow
Input Image
→
Audio-Image Matching
→
Select Audio Features
→
Latent Diffusion (4 Steps)
→
Generated Sound Effects
| Model | # Params | RTF | FAD ↓ | KL ↓ | IS ↑ |
|---|---|---|---|---|---|
| AudioLDM2 | 1397M | 5741G/s | 3.403 | 4.380 | 2.770 |
| MMAudio | 3163M | 4764G/s | 0.909 | 2.394 | 6.872 |
| Ours | 100M | 41G/s | 0.907 | 2.214 | 6.425 |
Real-time Ambient Sound Generation for Mobile Photo Galleries
A user uploads a photo of a beach scene. Our system instantly identifies 'beach' and 'wave' categories from its Audio Feature Dictionary. Leveraging the Multi-Category Generation, it synthesizes a rich soundscape combining ambient ocean waves with occasional seagull calls. The Generation Flow Map ensures the wave sounds swell and recede naturally, enhancing the visual experience with perfectly synchronized audio, all processed on the user's smartphone in less than half a second.
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Implementation Roadmap
A clear path to integrating AI into your enterprise, designed for rapid deployment and measurable impact.
Phase 1: Feature Dictionary & Matching Pipeline Setup
Establish the Audio Feature Dictionary and train the Audio-Image Matching Network.
Duration: 30 days
Phase 2: Lightweight Model Distillation
Implement knowledge distillation for VAE, U-Net, and Vocoder, optimizing for on-device performance.
Duration: 45 days
Phase 3: Multi-Category & Flow Map Integration
Integrate Multi-Category Generation and Generation Flow Map for diverse and controlled soundscapes.
Duration: 30 days
Phase 4: On-Device Deployment & Testing
Final optimization, deployment to target mobile platforms, and comprehensive user acceptance testing.
Duration: 20 days
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