3 papers
cs.SD2026
CORA: A Protocol for Diagnosing Boundary Robustness in Text-to-Audio Retrieval under Query Reformulations
Jae Min Woo, Kyongmin Kong, Bogyung Jeong +3
Text-to-Audio (T2A) retrievers are typically evaluated with caption style queries, but the same user intent can be expressed in many forms. We introduce CORA (Caption-Offset Retrie…
cs.SD2026
Omni-Embed-Audio: Leveraging Multimodal LLMs for Robust Audio-Text Retrieval
HaeJun Yoo, Yongseop Shin, Insung Lee +2
Audio-text retrieval systems based on Contrastive Language-Audio Pretraining (CLAP) achieve strong performance on traditional benchmarks; however, these benchmarks rely on caption-…
cs.SD2026
CAF-Score: Calibrating CLAP with LALMs for Reference-free Audio Captioning Evaluation
Insung Lee, Taeyoung Jeong, Haejun Yoo +2
While Large Audio-Language Models (LALMs) have advanced audio captioning, robust evaluation remains difficult. Reference-based metrics are expensive and often fail to assess acoust…