Self-Supervised Audio-Visual Soundscape Stylization (2409.14340v1)

Abstract: Speech sounds convey a great deal of information about the scenes, resulting in a variety of effects ranging from reverberation to additional ambient sounds. In this paper, we manipulate input speech to sound as though it was recorded within a different scene, given an audio-visual conditional example recorded from that scene. Our model learns through self-supervision, taking advantage of the fact that natural video contains recurring sound events and textures. We extract an audio clip from a video and apply speech enhancement. We then train a latent diffusion model to recover the original speech, using another audio-visual clip taken from elsewhere in the video as a conditional hint. Through this process, the model learns to transfer the conditional example's sound properties to the input speech. We show that our model can be successfully trained using unlabeled, in-the-wild videos, and that an additional visual signal can improve its sound prediction abilities. Please see our project webpage for video results: https://tinglok.netlify.app/files/avsoundscape/

• The paper introduces a novel self-supervised latent diffusion method to transfer environmental audio cues to speech for enhanced soundscape realism.
• It leverages a ResNet-based VAE and pre-trained CLAP/CLIP embeddings, conditioning the diffusion process via cross-attention on audio-visual inputs.
• Experimental evaluations on CityWalk and Acoustic-AVSpeech datasets reveal significant RT60 error reductions and superior subjective quality compared to baselines.

Self-Supervised Audio-Visual Soundscape Stylization

The paper under review, "Self-Supervised Audio-Visual Soundscape Stylization," addresses the complex problem of manipulating speech audio to match different environmental contexts based on given audio-visual cues. The proposed methodology leverages self-supervision to learn from natural video content, achieving significant improvements over existing solutions by capturing both acoustic and ambient sound properties inherent in various scenes. The model is designed to transfer these properties to an input speech, effectively stylizing it to fit diverse soundscape contexts.

Methodology Overview

The core approach revolves around a self-supervised learning model using a latent diffusion framework. Key steps include:

1. Data Preprocessing: Extract audio clips from videos, apply speech enhancement, and use source separation to isolate the clean foreground speech.
2. Model Training: A latent diffusion model is trained to recover the original speech from the enhanced version, using another audio-visual clip from a different time step within the same video as the conditional example.
3. Stylization Process: The trained model captures both acoustic and ambient sound properties from the conditional example and applies them to the input speech.

The model employs a ResNet-based VAE to convert mel-spectrograms into a latent space, facilitating efficient and high-quality adaptive sound generation. Pre-trained CLAP and CLIP models are used to extract audio and image embeddings, respectively, which are combined and used to condition the diffusion model through cross-attention.

Quantitative and Qualitative Results

The experimental evaluation employed both the newly developed CityWalk dataset and the Acoustic-AVSpeech dataset. The model’s performance was assessed through rigorous quantitative metrics, including MSE, RT60 Error, PESQ, and AVC among others. The proposed method exhibited superior performance relative to several baselines such as AViTAR, Audio Analogy, and various captioning-based approaches. For instance, the paper reports that during quantitative evaluations on the CityWalk dataset, the model delivered a significant reduction in RT60 Error and FAD, which are crucial for achieving realistic soundscapes.

Subjective human evaluations corroborated the quantitative findings. The model received higher scores for overall quality, relation to ambient sounds, acoustic properties, and relevance to the visual context, reflecting its adeptness at generating realistic and context-appropriate audio.

Implications and Future Research

The implications of this work are both practical and theoretical. Practically, it opens new possibilities in various multimedia applications including movie dubbing, virtual reality, and immersive gaming, where dynamic and contextually accurate soundscapes enhance user experience. Theoretically, it advances the understanding of how multimodal signals can be integrated to achieve nuanced audio stylization.

Speculative Future Developments:

• Enhanced Context Awareness: Future improvements could integrate more sophisticated contextual understanding, potentially using more advanced visual cues and semantic information to provide deeper contextual audio insights.
• Broader Application Scope: Extending the model to handle more diverse non-speech sounds can further enhance its versatility.
• Real-time Applications: Advancing computational efficiency to enable real-time soundscape adaptation for live broadcasting and interactive media.
• Robustness and Scalability: Exploring new architectures to improve robustness against diverse and noisy environments, ensuring scalability to larger and more varied datasets.

Conclusion

The paper demonstrates a novel approach to audio-visual soundscape stylization using self-supervised learning. The model's robust framework, leveraging latent diffusion and pre-trained representations, consistently outperformed existing methods, particularly in capturing complex sound properties from in-the-wild videos. This work not only contributes a high-impact solution to the field of audio-visual learning but also offers a promising direction for further research and application in immersive and interactive multimedia environments.