4 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…
PAT: Parameter-Free Audio-Text Aligner to Boost Zero-Shot Audio Classification
Ashish Seth, Ramaneswaran Selvakumar, Sonal Kumar +2
Audio-Language Models (ALMs) have demonstrated remarkable performance in zero-shot audio classification. In this paper, we introduce PAT (Parameter-free Audio-Text aligner), a simp…
EH-MAM: Easy-to-Hard Masked Acoustic Modeling for Self-Supervised Speech Representation Learning
Ashish Seth, Ramaneswaran Selvakumar, S Sakshi +3
In this paper, we present EH-MAM (Easy-to-Hard adaptive Masked Acoustic Modeling), a novel self-supervised learning approach for speech representation learning. In contrast to the…
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…