3 citations · 15 across the 32 of their papers we have counts for
14 papers · 1 filter
SILA: Signal-to-Language Augmentation for Enhanced Control in Text-to-Audio Generation
Sonal Kumar, Prem Seetharaman, Justin Salamon +2
The field of text-to-audio generation has seen significant advancements, and yet the ability to finely control the acoustic characteristics of generated audio remains under-explore…
MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmark
S Sakshi, Utkarsh Tyagi, Sonal Kumar +6
The ability to comprehend audio--which includes speech, non-speech sounds, and music--is crucial for AI agents to interact effectively with the world. We present MMAU, a novel benc…
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…
Synthio: Augmenting Small-Scale Audio Classification Datasets with Synthetic Data
Sreyan Ghosh, Sonal Kumar, Zhifeng Kong +3
We present Synthio, a novel approach for augmenting small-scale audio classification datasets with synthetic data. Our goal is to improve audio classification accuracy with limited…
ReCLAP: Improving Zero Shot Audio Classification by Describing Sounds
Sreyan Ghosh, Sonal Kumar, Chandra Kiran Reddy Evuru +3
Open-vocabulary audio-language models, like CLAP, offer a promising approach for zero-shot audio classification (ZSAC) by enabling classification with any arbitrary set of categori…
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…
LipGER: Visually-Conditioned Generative Error Correction for Robust Automatic Speech Recognition
Sreyan Ghosh, Sonal Kumar, Ashish Seth +4
Visual cues, like lip motion, have been shown to improve the performance of Automatic Speech Recognition (ASR) systems in noisy environments. We propose LipGER (Lip Motion aided Ge…
ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract Descriptions
Sreyan Ghosh, Utkarsh Tyagi, Sonal Kumar +4
We present ABEX, a novel and effective generative data augmentation methodology for low-resource Natural Language Understanding (NLU) tasks. ABEX is based on ABstract-and-EXpand, a…
Visual Description Grounding Reduces Hallucinations and Boosts Reasoning in LVLMs
Sreyan Ghosh, Chandra Kiran Reddy Evuru, Sonal Kumar +4
Large Vision-Language Models (LVLMs) often produce responses that misalign with factual information, a phenomenon known as hallucinations. While hallucinations are well-studied, th…
Do Vision-Language Models Understand Compound Nouns?
Sonal Kumar, Sreyan Ghosh, S Sakshi +2
Open-vocabulary vision-language models (VLMs) like CLIP, trained using contrastive loss, have emerged as a promising new paradigm for text-to-image retrieval. However, do VLMs unde…
CoDa: Constrained Generation based Data Augmentation for Low-Resource NLP
Chandra Kiran Reddy Evuru, Sreyan Ghosh, Sonal Kumar +3
We present CoDa (Constrained Generation based Data Augmentation), a controllable, effective, and training-free data augmentation technique for low-resource (data-scarce) NLP. Our a…
A Closer Look at the Limitations of Instruction Tuning
Sreyan Ghosh, Chandra Kiran Reddy Evuru, Sonal Kumar +5
Instruction Tuning (IT), the process of training large language models (LLMs) using instruction-response pairs, has emerged as the predominant method for transforming base pre-trai…