most citedEnhancing Zero-shot Audio Classification using Sound Attribute Knowledge from Large Language Models

1 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.SD2025

UniFlow-Audio: Unified Flow Matching for Audio Generation from Omni-Modalities

Xuenan Xu, Jiahao Mei, Zihao Zheng +9

Audio generation, including speech, music and sound effects, has advanced rapidly in recent years. These tasks can be divided into two categories: time-aligned (TA) tasks, where ea…

cs.CL2025★ 1 cited

MM-StoryAgent: Immersive Narrated Storybook Video Generation with a Multi-Agent Paradigm across Text, Image and Audio

Xuenan Xu, Jiahao Mei, Chenliang Li +5

The rapid advancement of large language models (LLMs) and artificial intelligence-generated content (AIGC) has accelerated AI-native applications, such as AI-based storybooks that…

cs.AI2025

WritingBench: A Comprehensive Benchmark for Generative Writing

Yuning Wu, Jiahao Mei, Ming Yan +8

Recent advancements in large language models (LLMs) have significantly enhanced text generation capabilities, yet evaluating their performance in generative writing remains a chall…

cs.SD2024★ 1 cited

Enhancing Zero-shot Audio Classification using Sound Attribute Knowledge from Large Language Models

Xuenan Xu, Pingyue Zhang, Ming Yan +2

Zero-shot audio classification aims to recognize and classify a sound class that the model has never seen during training. This paper presents a novel approach for zero-shot audio…

cs.SD2024

DiveSound: LLM-Assisted Automatic Taxonomy Construction for Diverse Audio Generation

Baihan Li, Zeyu Xie, Xuenan Xu +5

Audio generation has attracted significant attention. Despite remarkable enhancement in audio quality, existing models overlook diversity evaluation. This is partially due to the l…