DegDiT: Controllable Audio Generation with Dynamic Event Graph Guided Diffusion Transformer

• URL: http://arxiv.org/abs/2508.13786v1
• Date: Tue, 19 Aug 2025 12:41:15 GMT
• Title: DegDiT: Controllable Audio Generation with Dynamic Event Graph Guided Diffusion Transformer
• Authors: Yisu Liu, Chenxing Li, Wanqian Zhang, Wenfu Wang, Meng Yu, Ruibo Fu, Zheng Lin, Weiping Wang, Dong Yu,
• Abstract summary: We propose DegDiT, a novel dynamic event graph-guided diffusion transformer framework for controllable audio generation.<n>DegDiT encodes the events in the description as structured dynamic graphs.<n>Experiments on AudioCondition, DESED, and AudioTime datasets demonstrate that DegDiT achieves state-of-the-art performances.
• Score: 43.48616092324736
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Controllable text-to-audio generation aims to synthesize audio from textual descriptions while satisfying user-specified constraints, including event types, temporal sequences, and onset and offset timestamps. This enables precise control over both the content and temporal structure of the generated audio. Despite recent progress, existing methods still face inherent trade-offs among accurate temporal localization, open-vocabulary scalability, and practical efficiency. To address these challenges, we propose DegDiT, a novel dynamic event graph-guided diffusion transformer framework for open-vocabulary controllable audio generation. DegDiT encodes the events in the description as structured dynamic graphs. The nodes in each graph are designed to represent three aspects: semantic features, temporal attributes, and inter-event connections. A graph transformer is employed to integrate these nodes and produce contextualized event embeddings that serve as guidance for the diffusion model. To ensure high-quality and diverse training data, we introduce a quality-balanced data selection pipeline that combines hierarchical event annotation with multi-criteria quality scoring, resulting in a curated dataset with semantic diversity. Furthermore, we present consensus preference optimization, facilitating audio generation through consensus among multiple reward signals. Extensive experiments on AudioCondition, DESED, and AudioTime datasets demonstrate that DegDiT achieves state-of-the-art performances across a variety of objective and subjective evaluation metrics.

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