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Swapnil Bhosale

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3 papers

1 author row

AAAI Conference 2025 Conference Paper

Unsupervised Audio-Visual Segmentation with Modality Alignment

Swapnil Bhosale
Haosen Yang
Diptesh Kanojia
Jiankang Deng
Xiatian Zhu

Audio-Visual Segmentation (AVS) aims to identify, at the pixel level, the object in a visual scene that produces a given sound. Current AVS methods rely on costly fine-grained annotations of mask-audio pairs, making them impractical for scalability. To address this, we propose the Modality Correspondence Alignment (MoCA) framework, which seamlessly integrates off-the-shelf foundation models like DINO, SAM, and ImageBind. Our approach leverages existing knowledge within these models and optimizes their joint usage for multimodal associations. Our approach relies on estimating positive and negative image pairs in the feature space. For pixel-level association, we introduce an audio-visual adapter and a novel {pixel matching aggregation} strategy within the image-level contrastive learning framework. This allows for a flexible connection between object appearance and audio signal at the pixel level, with tolerance to imaging variations such as translation and rotation. Extensive experiments on the AVSBench (single and multi-object splits) and AVSS datasets demonstrate that MoCA outperforms unsupervised baseline approaches and some supervised counterparts, particularly in complex scenarios with multiple auditory objects. In terms of mIoU, MoCA achieves a substantial improvement over baselines in both the AVSBench (S4: +17.24%, MS3: +67.64%) and AVSS (+19.23%) audio-visual segmentation challenges.

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NeurIPS Conference 2024 Conference Paper

AV-GS: Learning Material and Geometry Aware Priors for Novel View Acoustic Synthesis

Swapnil Bhosale
Haosen Yang
Diptesh Kanojia
Jiankang Deng
Xiatian Zhu

Novel view acoustic synthesis (NVAS) aims to render binaural audio at any target viewpoint, given a mono audio emitted by a sound source at a 3D scene. Existing methods have proposed NeRF-based implicit models to exploit visual cues as a condition for synthesizing binaural audio. However, in addition to low efficiency originating from heavy NeRF rendering, these methods all have a limited ability of characterizing the entire scene environment such as room geometry, material properties, and the spatial relation between the listener and sound source. To address these issues, we propose a novel Audio-Visual Gaussian Splatting (AV-GS) model. To obtain a material-aware and geometry-aware condition for audio synthesis, we learn an explicit point-based scene representation with audio-guidance parameters on locally initialized Gaussian points, taking into account the space relation from the listener and sound source. To make the visual scene model audio adaptive, we propose a point densification and pruning strategy to optimally distribute the Gaussian points, with the per-point contribution in sound propagation (e. g. , more points needed for texture-less wall surfaces as they affect sound path diversion). Extensive experiments validate the superiority of our AV-GS over existing alternatives on the real-world RWAS and simulation-based SoundSpaces datasets. Project page: \url{https: //surrey-uplab. github. io/research/avgs/}

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AAAI Conference 2024 Conference Paper

DiffSED: Sound Event Detection with Denoising Diffusion

Swapnil Bhosale
Sauradip Nag
Diptesh Kanojia
Jiankang Deng
Xiatian Zhu

Sound Event Detection (SED) aims to predict the temporal boundaries of all the events of interest and their class labels, given an unconstrained audio sample. Taking either the split-and-classify (i.e., frame-level) strategy or the more principled event-level modeling approach, all existing methods consider the SED problem from the discriminative learning perspective. In this work, we reformulate the SED problem by taking a generative learning perspective. Specifically, we aim to generate sound temporal boundaries from noisy proposals in a denoising diffusion process, conditioned on a target audio sample. During training, our model learns to reverse the noising process by converting noisy latent queries to the ground-truth versions in the elegant Transformer decoder framework. Doing so enables the model generate accurate event boundaries from even noisy queries during inference. Extensive experiments on the Urban-SED and EPIC-Sounds datasets demonstrate that our model significantly outperforms existing alternatives, with 40+% faster convergence in training. Code: https://github.com/Surrey-UPLab/DiffSED

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