8 papers
MECAT: A Multi-Experts Constructed Benchmark for Fine-Grained Audio Understanding Tasks
Yadong Niu, Tianzi Wang, Heinrich Dinkel +7
While large audio-language models have advanced open-ended audio understanding, they still fall short of nuanced human-level comprehension. This gap persists largely because curren…
DashengTokenizer: One layer is enough for unified audio understanding and generation
Heinrich Dinkel, Xingwei Sun, Gang Li +8
This paper introduces DashengTokenizer, a continuous audio tokenizer engineered for joint use in both understanding and generation tasks. Unlike conventional approaches, which trai…
MiDashengLM: Efficient Audio Understanding with General Audio Captions
Heinrich Dinkel, Gang Li, Jizhong Liu +7
Current approaches for large audio language models (LALMs) often rely on closed data sources or proprietary models, limiting their generalization and accessibility. This paper intr…
ACAVCaps: Enabling large-scale training for fine-grained and diverse audio understanding
Yadong Niu, Tianzi Wang, Heinrich Dinkel +6
General audio understanding is a fundamental goal for large audio-language models, with audio captioning serving as a cornerstone task for their development. However, progress in t…
GLAP: General contrastive audio-text pretraining across domains and languages
Heinrich Dinkel, Zhiyong Yan, Tianzi Wang +7
Contrastive Language Audio Pretraining (CLAP) is a widely-used method to bridge the gap between audio and text domains. Current CLAP methods enable sound and music retrieval in Eng…
Reinforcement Learning Outperforms Supervised Fine-Tuning: A Case Study on Audio Question Answering
Gang Li, Jizhong Liu, Heinrich Dinkel +3
Recently, reinforcement learning (RL) has been shown to greatly enhance the reasoning capabilities of large language models (LLMs), and RL-based approaches have been progressively…