Text-to-Audio Generation Aligned with Videos: T2AV-BENCH

Core Concepts

The author introduces T2AV-BENCH, a benchmark for text-to-audio generation aligned with videos, and proposes the T2AV model that integrates visual-aligned text embeddings for improved audio synthesis.

Abstract

The content discusses the challenges in maintaining synchronization between generated audio and video frames. It introduces innovative methods like T2AV-BENCH and T2AV to address these issues. Extensive experiments and ablation studies validate the effectiveness of the proposed approach in achieving visual alignment and temporal consistency in text-to-audio generation.

Key Points:

• Introduction of T2AV-BENCH for video-aligned text-to-audio generation.
• Proposal of the T2AV model integrating visual-aligned text embeddings.
• Empirical experiments demonstrating state-of-the-art performance.
• Ablation studies highlighting the importance of visual-aligned CLAP and Audio-Visual ControlNet.
• Exploration of training data scale and latent diffusion tuning effects.

Stats

Our method significantly outperforms previous baselines in terms of all metrics (lower is better).
Extensive evaluations on the AudioCaps and T2AV-BENCH demonstrate that our T2AV sets a new standard for video-aligned TTA generation.

Quotes

"Our contributions can be summarized as presenting a novel benchmark for Text-to-Audio generation aligned with Video, introducing a simple yet effective approach called T2AV, and demonstrating state-of-the-art superiority over previous baselines."
"We achieve significant performance gains compared to MMDiffusion, DiffSound, AudioGen, and AudioLDM in video-aligned text-to-audio generation."

Key Insights Distilled From

Text-to-Audio Generation Synchronized with Videos

by Shentong Mo,... at arxiv.org 03-14-2024

https://arxiv.org/pdf/2403.07938.pdf

Deeper Inquiries

How might biases be embedded in the data used for training this system

Biases can be embedded in the data used for training this system through several mechanisms. One common source of bias is the dataset itself, as it may not be representative of all sound categories or may contain imbalances in certain classes. For example, if the training data predominantly consists of common sound categories like "dog barking" or "car honking," the model may struggle to accurately generate audio for less common sound categories. Additionally, biases can also arise from human annotations or labeling errors in the dataset, leading to skewed representations and potentially affecting the model's performance on underrepresented sound categories.

What are potential challenges when generating audio for less common sound categories

Generating audio for less common sound categories poses several challenges. One major challenge is the lack of sufficient training data for these specific categories, which can result in limited diversity and variability in generating accurate audio representations. The model may struggle to capture nuanced details and characteristics unique to these less common sounds due to a lack of exposure during training. Furthermore, there might be fewer examples available for fine-tuning or adjusting model parameters specifically tailored to these niche sound categories, making it harder to achieve high-quality audio generation results compared to more prevalent classes.

How important is it to maintain temporal consistency between audio and video frames in real-world applications

Maintaining temporal consistency between audio and video frames is crucial in real-world applications where synchronized multimedia content is essential. In scenarios such as video production, virtual reality experiences, live broadcasts, or interactive media platforms, any discrepancies between audio and visual elements can lead to a disjointed user experience and detract from immersion and engagement levels. Ensuring that audio events align precisely with corresponding visual cues enhances realism and coherence within multimedia content delivery systems while providing a seamless viewing/listening experience for users across various platforms.