19 citations · 137 across the 64 of their papers we have counts for
10 papers · 1 filter
Audio-Visual Flamingo: Open Audio-Visual Intelligence for Long and Complex Videos
Sreyan Ghosh, Arushi Goel, Kaousheik Jayakumar +19
We present Audio-Visual Flamingo (AV-Flamingo), a fully open state-of-the-art audio-visual large language model (AV-LLM) for joint understanding and reasoning over audio, images, a…
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…
Room Impulse Response Synthesis via Differentiable Feedback Delay Networks for Efficient Spatial Audio Rendering
Armin Gerami, Ramani Duraiswami
We introduce a computationally efficient and tunable feedback delay network (FDN) architecture for real-time room impulse response (RIR) rendering that addresses the computational…
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…
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…
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…
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…
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…
RECAP: Retrieval-Augmented Audio Captioning
Sreyan Ghosh, Sonal Kumar, Chandra Kiran Reddy Evuru +2
We present RECAP (REtrieval-Augmented Audio CAPtioning), a novel and effective audio captioning system that generates captions conditioned on an input audio and other captions simi…