Title: MECAT: A Multi-Experts Constructed  Benchmark for Fine-Grained Audio Understanding Tasks
Primary area: datasets and benchmarks
Keywords: Fine-Grained Audio Understanding, Benchmark, Large Audio-Language Models, Discriminative Evaluation Metric, Audio Caption, Audio Question Answering

Abstract: 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 highly detailed model outputs. To this end, this work introduces MECAT, a Multi-Expert Constructed Benchmark for Fine-Grained Audio Understanding Tasks. Generated via a pipeline that integrates analysis from specialized expert models with Chain-of-Thought large language model reasoning, MECAT provides multi-perspective, fine-grained captions and open-set question-answering pairs. The benchmark is complemented by a novel metric: DATE (Discriminative-Enhanced AudioText Evaluation). This metric penalizes generic terms and rewards detailed descriptions by combining single-sample semantic similarity with cross-sample discriminability. A comprehensive evaluation of state-of-the-art audio models is also presented, providing new insights into their current capabilities and limitations. The data and code will be made publicly available.

TL;DR: To effectively distinguish between vague and precise outputs from large audio-language models, we constructed the MECAT benchmark, which comprises a novel, fine-grained annotated dataset and its accompanying discriminative evaluation metric.