SEE-2-SOUND: Zero-Shot Spatial Environment-to-Spatial Sound (2406.06612v1)

Abstract: Generating combined visual and auditory sensory experiences is critical for the consumption of immersive content. Recent advances in neural generative models have enabled the creation of high-resolution content across multiple modalities such as images, text, speech, and videos. Despite these successes, there remains a significant gap in the generation of high-quality spatial audio that complements generated visual content. Furthermore, current audio generation models excel in either generating natural audio or speech or music but fall short in integrating spatial audio cues necessary for immersive experiences. In this work, we introduce SEE-2-SOUND, a zero-shot approach that decomposes the task into (1) identifying visual regions of interest; (2) locating these elements in 3D space; (3) generating mono-audio for each; and (4) integrating them into spatial audio. Using our framework, we demonstrate compelling results for generating spatial audio for high-quality videos, images, and dynamic images from the internet, as well as media generated by learned approaches.

• The paper introduces a novel task for generating synchronized spatial audio from visual inputs by decomposing the process into segmentation, depth estimation, mono-audio generation, and spatial synthesis.
• It leverages state-of-the-art models like Segment Anything and CoDi to achieve zero-shot generalization without additional fine-tuning.
• Evaluations demonstrate significant improvements in audio realism and immersion, indicating strong potential for applications in VR/AR and interactive multimedia.

An Essay on SEE-2-SOUND: Zero-Shot Spatial Environment-to-Spatial Sound

The paper "SEE-2-SOUND: Zero-Shot Spatial Environment-to-Spatial Sound" addresses a significant gap in the field of audio generation — the production of high-quality spatial audio that augments visual content. The primary innovation of SEE-2-SOUND is its zero-shot approach which employs existing models in a novel framework to generate spatial audio from images, animated images, and videos. This work is positioned to enhance the immersive experience and contextual understanding of visual content where synchronized spatial audio cues play a crucial role.

Core Contributions

The authors delineate several contributions that differentiate their model from previous works:

1. Introduction of a New Task: The paper frames and tackles the novel task of generating spatial audio from visual inputs. Unlike prior efforts primarily centered around text-to-audio or video-to-audio tasks, SEE-2-SOUND uniquely focuses on spatial audio generation.
2. Compositional Framework: SEE-2-SOUND's architecture decomposes the audio generation process into distinct sub-tasks: identifying regions of interest within the visual input, estimating their 3D spatial positions, generating corresponding mono-audio, and then synthesizing this information into spatial audio. This modular approach leverages state-of-the-art models like Segment Anything, Depth Anything, and CoDi, ensuring flexibility and extensibility.
3. Zero-Shot Generalization: By utilizing pre-trained models without further fine-tuning, SEE-2-SOUND exhibits zero-shot generalization capabilities, significantly simplifying the deployment and adaptation process for diverse application scenarios.
4. Quantitative and Qualitative Evaluation: The paper introduces both quantitative and qualitative evaluation metrics for a comprehensive assessment of generated spatial audio. This evaluation includes a unique method using AViTAR for scene-guided audio generation and subsequent perceptual studies.

Detailed Methodology

The authors propose a four-stage pipeline for spatial audio generation:

1. Source Estimation: This step entails segmenting the input images to identify regions of interest and estimating their 3D spatial coordinates via depth estimation. These steps utilize Segment Anything for segmentation and Depth Anything for depth estimation, producing positional data for different elements within the scene.
2. Audio Generation: The identified regions of interest and optional text prompts are then mapped to mono-audio generation using CoDi, an advanced conditional Latent Diffusion Model (LDM). The model generates mono-audio segments that correspond to the regions of interest.
3. Spatial Audio Synthesis: The mono-audio segments and their positional information are used to simulate a 5.1 surround sound setup within a virtual "ShoeBox" room. This spatialization process adheres to ITU-R BS 775 standards and involves calculating Room Impulse Responses (RIRs) using the Image Source Method (ISM).
4. Integration and Synthesis: The synthesized signals are convolved to produce spatial audio, incorporating the unique characteristics of multi-source interference crucial for realistic soundscapes.

Evaluation and Results

The paper's rigorous evaluation approach includes both quantitative metrics — MFCC-DTW for temporal consistency, ZCR for transient details, Chroma for pitch class profiles, and Spectral Contrast for harmonic traits — and a comprehensive human paper. Key findings indicate:

• SEE-2-SOUND yields substantial improvements in audio similarity metrics compared to baseline mono-audio models.
• Human evaluators reported statistically significant preferences for SEE-2-SOUND's output in terms of realism, immersion, accuracy, clarity, and consistency.

Implications and Future Directions

SEE-2-SOUND advances both practical and theoretical horizons in AI-driven content generation. Practically, its potential applications extend to enhancing user experience in virtual reality (VR) and augmented reality (AR), interactive multimedia, and accessibility technologies where spatial audio enhances immersion and realism. Theoretically, this work sets a precedent in decomposing complex generative tasks into compositional sub-tasks, leveraging cutting-edge models for each sub-task to achieve zero-shot generalization.

Future research could explore real-time capabilities, refine the detection of fine visual details, and integrate dynamic motion cues to further enhance audio generation. Additionally, addressing ethical concerns around the potential misuse of generative technologies for creating deepfakes remains paramount.

Conclusion

"SEE-2-SOUND: Zero-Shot Spatial Environment-to-Spatial Sound" presents a substantial step forward in the field of multimodal content generation by proposing a novel and effective framework for spatial audio generation from visual inputs. By leveraging existing models in a zero-shot setup, SEE-2-SOUND showcases impressive results in generating immersive and realistic spatial audio, setting the stage for future innovation and application in the field.