Hear What Matters! Text-conditioned Selective Video-to-Audio Generation
Abstract
This work introduces a new task, text-conditioned selective video-to-audio (V2A) generation, which produces only the user-intended sound from a multi-object video. This capability is especially crucial in multimedia production, where audio tracks are handled individually for each sound source for precise editing, mixing, and creative control. However, current approaches generate single source-mixed sounds at once, largely because visual features are entangled, and region cues or prompts often fail to specify the source. We propose SelVA, a novel text-conditioned V2A model that treats the text prompt as an explicit selector of target source and modulates video encoder to distinctly extract prompt-relevant video features. The proposed supplementary tokens promote cross-attention by suppressing text-irrelevant activations with efficient parameter tuning, yielding robust semantic and temporal grounding. SelVA further employs a self-augmentation scheme to overcome the lack of mono audio track supervision. We evaluate SelVA on VGG-MONOAUDIO, a curated benchmark of clean single-source videos for such a task. Extensive experiments and ablations consistently verify its effectiveness across audio quality, semantic alignment, and temporal synchronization. Code and demo are available at https://jnwnlee.github.io/selva-demo/.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of a novel task and model for text-conditioned selective video-to-audio generation, which significantly enhances the ability to isolate and generate specific audio sources from videos. The methodology and experimental results indicate a meaningful advancement in the field, although further comparisons with existing methods and more detailed implementation guidelines would strengthen the overall impact.
Methodology
The proposed SelVA model introduces a novel approach to text-conditioned selective video-to-audio generation, which is a significant advancement in the field. The methodology effectively utilizes text prompts as selectors to modulate video encoders, allowing for the extraction of relevant audio features corresponding to specific visual cues. The introduction of supplementary tokens to enhance cross-attention and suppress irrelevant activations is particularly innovative. However, the paper could benefit from a more detailed explanation of the self-augmentation scheme and how it addresses the lack of mono audio track supervision.
Results & evaluation
The experiments conducted on the VGG-MONOAUDIO dataset are extensive and demonstrate the effectiveness of SelVA in terms of audio quality, semantic alignment, and temporal synchronization. The ablation studies provide valuable insights into the contributions of different components of the model. However, the paper lacks a comparative analysis with existing state-of-the-art methods, which would strengthen the claims of superiority and effectiveness.
Reproducibility
The paper mentions that code and a demo are available, which is a positive aspect for reproducibility. However, the details regarding the implementation, including hyperparameters and training procedures, are not sufficiently discussed. This lack of detail may hinder other researchers from replicating the results.
Limitations
One limitation is the reliance on a curated benchmark dataset, which may not fully represent the diversity of real-world scenarios. Additionally, the model's performance in more complex video environments with overlapping sounds or dynamic scenes is not addressed, which could limit its applicability in practical settings.
Broader impact
The ability to generate selective audio from video has significant implications for multimedia production, enhancing creative control and editing precision. This technology could also find applications in areas such as virtual reality, gaming, and assistive technologies for the hearing impaired, making it a potentially transformative advancement in audio-visual content creation. The main contribution of this paper is the introduction of a novel task and model for text-conditioned selective video-to-audio generation, which significantly enhances the ability to isolate and generate specific audio sources from videos. The methodology and experimental results indicate a meaningful advancement in the field, although further comparisons with existing methods and more detailed implementation guidelines would strengthen the overall impact.
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