1 citations · 2 across the 12 of their papers we have counts for
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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…
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…
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…