54 citations · 83 across the 57 of their papers we have counts for
61 papers
SteerDuplex: Steerable Duplex Speech Dialogue Models
Utkarsh Tyagi, Ramaneswaran Selvakumar, Advait Gosai +13
Full-duplex spoken dialogue models support low-latency turn taking, interruption handling, and backchanneling, yet a key capability remains underexplored: steerability, the ability…
CyberForge: Verified Vulnerability Injection at Repository Level for Cybersecurity Agent Training
Amine Lbath, Manan Suri, Aurelien Delaitre +4
Despite recent advances, frontier large language model (LLM) agents remain limited in discovering and patching complex vulnerabilities in real-world software. Generally available a…
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…
Music Flamingo: Scaling Music Understanding in Audio Language Models
Sreyan Ghosh, Arushi Goel, Lasha Koroshinadze +8
We introduce Music Flamingo, a novel large audio-language model designed to advance music (including song) understanding in foundational audio models. While audio-language research…
SPUR: A Plug-and-Play Framework for Integrating Spatial Audio Understanding and Reasoning into Large Audio-Language Models
S Sakshi, Vaibhavi Lokegaonkar, Neil Zhang +4
Spatial perception is central to auditory intelligence, enabling accurate understanding of real-world acoustic scenes and advancing human-level perception of the world around us. W…
EGOILLUSION: Benchmarking Hallucinations in Egocentric Video Understanding
Ashish Seth, Utkarsh Tyagi, Ramaneswaran Selvakumar +6
Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in complex multimodal tasks. While MLLMs excel at visual perception and reasoning in third-person…
MMAU-Pro: A Challenging and Comprehensive Benchmark for Holistic Evaluation of Audio General Intelligence
Sonal Kumar, Šimon Sedláček, Vaibhavi Lokegaonkar +31
Audio comprehension-including speech, non-speech sounds, and music-is essential for achieving human-level intelligence. Consequently, AI agents must demonstrate holistic audio unde…
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…
MultiVox: A Benchmark for Evaluating Voice Assistants for Multimodal Interactions
Ramaneswaran Selvakumar, Ashish Seth, Nishit Anand +4
The rapid progress of Large Language Models (LLMs) has empowered omni models to act as voice assistants capable of understanding spoken dialogues. These models can process multimod…
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…
Learning Multi-Robot Coordination through Locality-Based Factorized Multi-Agent Actor-Critic Algorithm
Chak Lam Shek, Amrit Singh Bedi, Anjon Basak +5
In this work, we present a novel cooperative multi-agent reinforcement learning method called \textbf{Loc}ality based \textbf{Fac}torized \textbf{M}ulti-Agent \textbf{A}ctor-\textb…
ProSE: Diffusion Priors for Speech Enhancement
Sonal Kumar, Sreyan Ghosh, Utkarsh Tyagi +4
Speech enhancement (SE) is the foundational task of enhancing the clarity and quality of speech in the presence of non-stationary additive noise. While deterministic deep learning…
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…
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…
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…
Towards a Systematic Evaluation of Hallucinations in Large-Vision Language Models
Ashish Seth, Dinesh Manocha, Chirag Agarwal
Large Vision-Language Models (LVLMs) have demonstrated remarkable performance in complex multimodal tasks. However, these models still suffer from hallucinations, particularly when…
Immune: Improving Safety Against Jailbreaks in Multi-modal LLMs via Inference-Time Alignment
Soumya Suvra Ghosal, Souradip Chakraborty, Vaibhav Singh +7
With the widespread deployment of Multimodal Large Language Models (MLLMs) for visual-reasoning tasks, improving their safety has become crucial. Recent research indicates that des…
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…
Failing Forward: Improving Generative Error Correction for ASR with Synthetic Data and Retrieval Augmentation
Sreyan Ghosh, Mohammad Sadegh Rasooli, Michael Levit +4
Generative Error Correction (GEC) has emerged as a powerful post-processing method to enhance the performance of Automatic Speech Recognition (ASR) systems. However, we show that G…
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…
Code Comprehension then Auditing for Unsupervised LLM Evaluation
Bhrij Patel, Souradip Chakraborty, Mengdi Wang +2
Large Language Models (LLMs) for unsupervised code correctness evaluation have recently gained attention because they can judge if code runs as intended without requiring reference…
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…