15 citations · 24 across the 25 of their papers we have counts for
10 papers · 1 filter
FIGMA: Towards FIne-Grained Music retrievAl
Nishit Anand, Ashish Seth, Sreyan Ghosh +2
Retrieving music using natural language descriptions has improved with contrastive audio-text models such as CLAP, but current systems remain limited to coarse semantic queries. Wh…
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…
TSPE: Task-Specific Prompt Ensemble for Improved Zero-Shot Audio Classification
Nishit Anand, Ashish Seth, Ramani Duraiswami +1
Audio-language models (ALMs) excel in zero-shot audio classification, a task where models classify previously unseen audio clips at test time by leveraging descriptive natural lang…
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…
GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning Abilities
Sreyan Ghosh, Sonal Kumar, Ashish Seth +6
Perceiving and understanding non-speech sounds and non-verbal speech is essential to making decisions that help us interact with our surroundings. In this paper, we propose GAMA, a…
CompA: Addressing the Gap in Compositional Reasoning in Audio-Language Models
Sreyan Ghosh, Ashish Seth, Sonal Kumar +7
A fundamental characteristic of audio is its compositional nature. Audio-language models (ALMs) trained using a contrastive approach (e.g., CLAP) that learns a shared representatio…
DeLoRes: Decorrelating Latent Spaces for Low-Resource Audio Representation Learning
Sreyan Ghosh, Ashish Seth, and Deepak Mittal +2
Inspired by the recent progress in self-supervised learning for computer vision, in this paper we introduce DeLoRes, a new general-purpose audio representation learning approach. O…
DECAR: Deep Clustering for learning general-purpose Audio Representations
Sreyan Ghosh, Sandesh V Katta, Ashish Seth +1
We introduce DECAR, a self-supervised pre-training approach for learning general-purpose audio representations. Our system is based on clustering: it utilizes an offline clustering…