JenBridge: Adaptive Long-Form Video Soundtracking across Scene Transitions
Abstract
We address the challenge of generating high-fidelity, long-form soundtracks that remain coherent across scene transitions. Existing AI music systems are mainly designed for short, isolated clips and lack mechanisms to ensure narrative continuity. We present JenBridge, a modular and interpretable framework for adaptive long-form video soundtracking that ensures both high-fidelity audio generation and transition naturalness. The core architecture is a Transformer-based generative model trained with a flow-matching objective, following a two-stage paradigm: pretraining on large-scale text-audio corpora to establish robust musical priors, then adapting to the video domain with dual text-visual conditioning for precise cross-modal alignment. Crucially, to achieve long-form coherence across diverse scene changes, JenBridge incorporates a novel adaptive transition mechanism. This system features a versatile toolkit of transition styles, including a generative transition method, and uniquely employs a Large Language Model (LLM) Agent that acts as a director to select the most appropriate transition for each narrative shift intelligently. To rigorously assess this task, we propose the LVS Benchmark, a new benchmark that includes a curated dataset and novel evaluation metrics focusing on holistic and transition-aware assessment. Extensive experiments on the proposed benchmark demonstrate that JenBridge significantly outperforms existing methods in both objective and subjective metrics, particularly in terms of transition naturalness and overall narrative coherence. JenBridge represents a significant step towards fully automated, professional-quality video soundtracking.
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
JenBridge represents a significant advancement in the field of adaptive long-form video soundtracking, integrating innovative methodologies and robust evaluation frameworks to address the challenges of coherence and narrative continuity in AI-generated music. The comprehensive analysis of its technical contributions and the proposed benchmarks positions this work as a valuable resource for future research and applications in creative AI.
Methodology
The methodology presented in JenBridge is robust, employing a two-stage training paradigm that integrates a Transformer-based generative model with a flow-matching objective. The segmentation of video into semantically coherent clips allows for a more manageable approach to music generation, while the dual conditioning mechanism (text and visual) enhances cross-modal alignment. The introduction of an LLM agent as a director for adaptive transitions is particularly innovative, showcasing a sophisticated understanding of narrative coherence in soundtracking. The modularity and interpretability of the framework are significant strengths, allowing for creative control over the soundtracking process.
Results & evaluation
The experimental evaluation is thorough, utilizing both objective and subjective metrics to assess performance on the newly proposed LVS Benchmark. The results demonstrate a clear superiority of JenBridge over existing methods, particularly in transition naturalness and overall coherence. The use of a user study adds a valuable layer of validation to the findings, reinforcing the model's effectiveness in real-world applications. The ablation studies further substantiate the importance of each component in the framework, providing insight into the contributions of various design choices.
Reproducibility
The authors have committed to making the inference codes and the LVS Benchmark publicly available, which is a positive step towards ensuring reproducibility. However, the foundational text-to-music model's weights will not be released due to licensing constraints, which may limit full reproducibility of the results. The detailed descriptions of training procedures and methodologies contribute to a clearer understanding of the implementation, but the lack of access to the foundational model may hinder some aspects of reproducibility.
Limitations
The paper acknowledges limitations related to the quality of the video-music training datasets, which could affect the final output's fidelity. Additionally, the LLM agent's current scope is limited to local decision-making without a global narrative understanding, which could lead to suboptimal musical choices in complex scenes. The model also does not account for original audio elements, such as dialogue, which may impact the overall coherence of the soundtrack.
Broader impact
JenBridge has the potential to significantly impact the field of automated soundtracking, providing tools that can enhance the creative processes of video producers and filmmakers. By bridging the gap between automated generation and professional-quality production, it opens avenues for new applications in multimedia content creation. The ethical considerations regarding data usage and the intent to empower human creators rather than replace them are commendable and reflect a responsible approach to AI development. JenBridge represents a significant advancement in the field of adaptive long-form video soundtracking, integrating innovative methodologies and robust evaluation frameworks to address the challenges of coherence and narrative continuity in AI-generated music. The comprehensive analysis of its technical contributions and the proposed benchmarks positions this work as a valuable resource for future research and applications in creative AI.
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