1 citations · 2 across the 12 of their papers we have counts for
13 papers
MiDashengLM-Gen: Unified Audio Scene Generation via LLM-Driven Autoregressive Flow Matching
Xingwei Sun, Heinrich Dinkel, Gang Li +7
Generating coherent audio scenes that simultaneously blend speech, music, and sound effects remains a significant challenge. Current approaches typically rely on a disjointed pipel…
Dasheng AudioGen: A Unified Model for Generating Coherent Audio Scenes from Text
Jiahao Mei, Heinrich Dinkel, Yadong Niu +7
Audio generation has long been fragmented, with speech, music, and sound effects produced by domain-specific models that fail to jointly generate coherent audio scenes from a singl…
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…
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…
The Interspeech 2026 Audio Encoder Capability Challenge for Large Audio Language Models
Heinrich Dinkel, Jiahao Zhou, Guanbo Wang +8
This paper presents the Interspeech 2026 Audio Encoder Capability Challenge, a benchmark specifically designed to evaluate and advance the performance of pre-trained audio encoders…
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…
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…
Efficient Speech Enhancement via Embeddings from Pre-trained Generative Audioencoders
Xingwei Sun, Heinrich Dinkel, Yadong Niu +3
Recent research has delved into speech enhancement (SE) approaches that leverage audio embeddings from pre-trained models, diverging from time-frequency masking or signal predictio…
StarVC: A Unified Auto-Regressive Framework for Joint Text and Speech Generation in Voice Conversion
Fengjin Li, Jie Wang, Yadong Niu +4
Voice Conversion (VC) modifies speech to match a target speaker while preserving linguistic content. Traditional methods usually extract speaker information directly from speech wh…
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…
X-ARES: A Comprehensive Framework for Assessing Audio Encoder Performance
Junbo Zhang, Heinrich Dinkel, Yadong Niu +4
We introduces X-ARES (eXtensive Audio Representation and Evaluation Suite), a novel open-source benchmark designed to systematically assess audio encoder performance across diverse…
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…
The ICME 2025 Audio Encoder Capability Challenge
Junbo Zhang, Heinrich Dinkel, Qiong Song +8
This challenge aims to evaluate the capabilities of audio encoders, especially in the context of multi-task learning and real-world applications. Participants are invited to submit…