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Yadong Niu

3 accepted papers

2026

ACAVCAPS: ENABLING LARGE-SCALE TRAINING FOR FINE-GRAINED AND DIVERSE AUDIO UNDERSTANDING

ICASSP 2026poster

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 this domain is hindered by existing datasets, which lack the scale and descriptive granularity required to train truly ve…

Cited by 0SourcePDFScholar
2026

MECAT: A Multi-Experts Constructed Benchmark for Fine-Grained Audio Understanding Tasks

ICML 2026poster

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 current benchmarks, limited by data annotations and evaluation metrics, fail to reliably distinguish between generic and highl…

Cited by 0SourceScholar
2023

A Model-Based Hearing Compensation Method Using a Self-Supervised Framework

ICASSP 2023accepted

Hearing aids can improve auditory perception for hearing-impaired (HI) listeners, but even state-of-art devices provide only limited benefits if not configured correctly for the listeners. The prescriptive fittings of hearing aids ignore the individual difference among HI listeners with identical he…

Cited by 0SourceScholar