9 papers · 1 filter
Audio Hallucination Attacks: Probing the Reliability of Large Audio Language Models
Ashish Seth, Sonal Kumar, Ramaneswaran Selvakumar +5
Large Audio Language Models (LALMs) achieve strong performance on audio-language tasks; however, their reliability in real-world settings remains underexplored. We introduce Audio…
Multi-Domain Audio Question Answering Benchmark Toward Acoustic Content Reasoning
Chao-Han Huck Yang, Sreyan Ghosh, Qing Wang +14
We present Task 5 of the DCASE 2025 Challenge: an Audio Question Answering (AQA) benchmark spanning multiple domains of sound understanding. This task defines three QA subsets (Bio…
TAC: Timestamped Audio Captioning
Sonal Kumar, Prem Seetharaman, Ke Chen +8
Large Audio Language Models struggle to disentangle overlapping events in complex acoustic scenes, yielding temporally inconsistent captions and frequent hallucinations. We introdu…
Audio Flamingo 3: Advancing Audio Intelligence with Fully Open Large Audio Language Models
Arushi Goel, Sreyan Ghosh, Jaehyeon Kim +8
We present Audio Flamingo 3 (AF3), a fully open state-of-the-art (SOTA) large audio-language model that advances reasoning and understanding across speech, sound, and music. AF3 in…
Audio Flamingo 2: An Audio-Language Model with Long-Audio Understanding and Expert Reasoning Abilities
Sreyan Ghosh, Zhifeng Kong, Sonal Kumar +6
Understanding and reasoning over non-speech sounds and music are crucial for both humans and AI agents to interact effectively with their environments. In this paper, we introduce…
Do Audio-Language Models Understand Linguistic Variations?
Ramaneswaran Selvakumar, Sonal Kumar, Hemant Kumar Giri +4
Open-vocabulary audio language models (ALMs), like Contrastive Language Audio Pretraining (CLAP), represent a promising new paradigm for audio-text retrieval using natural language…