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X-posing Free Speech: Examining the Impact of Moderation Relaxation on Online Social Networks

Autor: Arun, Arvindh, Chhatani, Saurav, An, Jisun, Kumaraguru, Ponnurangam

We investigate the impact of free speech and the relaxation of moderation on online social media platforms using Elon Musk's takeover of Twitter as a case study. By curating a dataset of over 10 million tweets, our study employs a novel framework com

Externí odkaz: http://arxiv.org/abs/2404.11465

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Ensemble of Task-Specific Language Models for Brain Encoding

Autor: Arun, Arvindh, John, Jerrin, Kumaran, Sanjai

Language models have been shown to be rich enough to encode fMRI activations of certain Regions of Interest in our Brains. Previous works have explored transfer learning from representations learned for popular natural language processing tasks for p

Externí odkaz: http://arxiv.org/abs/2310.15720

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CAFIN: Centrality Aware Fairness inducing IN-processing for Unsupervised Representation Learning on Graphs

Autor: Arun, Arvindh, Aanegola, Aakash, Agrawal, Amul, Narayanam, Ramasuri, Kumaraguru, Ponnurangam

Unsupervised Representation Learning on graphs is gaining traction due to the increasing abundance of unlabelled network data and the compactness, richness, and usefulness of the representations generated. In this context, the need to consider fairne

Externí odkaz: http://arxiv.org/abs/2304.04391

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Semi-supervised Time Domain Target Speaker Extraction with Attention

Autor: Wang, Zhepei, Giri, Ritwik, Venkataramani, Shrikant, Isik, Umut, Valin, Jean-Marc, Smaragdis, Paris, Goodwin, Mike, Krishnaswamy, Arvindh

In this work, we propose Exformer, a time-domain architecture for target speaker extraction. It consists of a pre-trained speaker embedder network and a separator network based on transformer encoder blocks. We study multiple methods to combine speak

Externí odkaz: http://arxiv.org/abs/2206.09072

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To Dereverb Or Not to Dereverb? Perceptual Studies On Real-Time Dereverberation Targets

Autor: Valin, Jean-Marc, Giri, Ritwik, Venkataramani, Shrikant, Isik, Umut, Krishnaswamy, Arvindh

In real life, room effect, also known as room reverberation, and the present background noise degrade the quality of speech. Recently, deep learning-based speech enhancement approaches have shown a lot of promise and surpassed traditional denoising a

Externí odkaz: http://arxiv.org/abs/2206.07917

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Real-Time Packet Loss Concealment With Mixed Generative and Predictive Model

Autor: Valin, Jean-Marc, Mustafa, Ahmed, Montgomery, Christopher, Terriberry, Timothy B., Klingbeil, Michael, Smaragdis, Paris, Krishnaswamy, Arvindh

As deep speech enhancement algorithms have recently demonstrated capabilities greatly surpassing their traditional counterparts for suppressing noise, reverberation and echo, attention is turning to the problem of packet loss concealment (PLC). PLC i

Externí odkaz: http://arxiv.org/abs/2205.05785

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