b'Anurag Kumar'

research

∙ 07/31/2023

DAVIS: High-Quality Audio-Visual Separation with Generative Diffusion Models

We propose DAVIS, a Diffusion model-based Audio-VIusal Separation framew...

0 Chao Huang, et al. ∙

research

∙ 03/23/2023

Egocentric Audio-Visual Object Localization

Humans naturally perceive surrounding scenes by unifying sound and sight...

0 Chao Huang, et al. ∙

research

∙ 02/16/2023

PAAPLoss: A Phonetic-Aligned Acoustic Parameter Loss for Speech Enhancement

Despite rapid advancement in recent years, current speech enhancement mo...

0 Muqiao Yang, et al. ∙

research

∙ 02/16/2023

TAPLoss: A Temporal Acoustic Parameter Loss for Speech Enhancement

Speech enhancement models have greatly progressed in recent years, but s...

0 Yunyang Zeng, et al. ∙

research

∙ 02/04/2023

AV-NeRF: Learning Neural Fields for Real-World Audio-Visual Scene Synthesis

Human perception of the complex world relies on a comprehensive analysis...

0 Susan Liang, et al. ∙

research

∙ 01/11/2023

Rethinking complex-valued deep neural networks for monaural speech enhancement

Despite multiple efforts made towards adopting complex-valued deep neura...

0 Haibin Wu, et al. ∙

research

∙ 11/20/2022

LA-VocE: Low-SNR Audio-visual Speech Enhancement using Neural Vocoders

Audio-visual speech enhancement aims to extract clean speech from a nois...

0 Rodrigo Mira, et al. ∙

research

∙ 11/16/2022

Leveraging Heteroscedastic Uncertainty in Learning Complex Spectral Mapping for Single-channel Speech Enhancement

Most speech enhancement (SE) models learn a point estimate, and do not m...

0 Kuan-Lin Chen, et al. ∙

research

∙ 07/01/2022

Improving Speech Enhancement through Fine-Grained Speech Characteristics

While deep learning based speech enhancement systems have made rapid pro...

0 Muqiao Yang, et al. ∙

research

∙ 06/24/2022

SAQAM: Spatial Audio Quality Assessment Metric

Audio quality assessment is critical for assessing the perceptual realis...

0 Pranay Manocha, et al. ∙

research

∙ 06/24/2022

Speech Quality Assessment through MOS using Non-Matching References

Human judgments obtained through Mean Opinion Scores (MOS) are the most ...

0 Pranay Manocha, et al. ∙

research

∙ 02/17/2022

Curriculum optimization for low-resource speech recognition

Modern end-to-end speech recognition models show astonishing results in ...

0 Anastasia Kuznetsova, et al. ∙

research

∙ 02/17/2022

RemixIT: Continual self-training of speech enhancement models via bootstrapped remixing

We present RemixIT, a simple yet effective self-supervised method for tr...

8 Efthymios Tzinis, et al. ∙

research

∙ 12/08/2021

NICE-Beam: Neural Integrated Covariance Estimators for Time-Varying Beamformers

Estimating a time-varying spatial covariance matrix for a beamforming al...

0 Jonah Casebeer, et al. ∙

research

∙ 10/25/2021

Multichannel Speech Enhancement without Beamforming

Deep neural networks are often coupled with traditional spatial filters,...

0 Asutosh Pandey, et al. ∙

research

∙ 10/22/2021

TADRN: Triple-Attentive Dual-Recurrent Network for Ad-hoc Array Multichannel Speech Enhancement

Deep neural networks (DNNs) have been successfully used for multichannel...

0 Ashutosh Pandey, et al. ∙

research

∙ 10/20/2021

TPARN: Triple-path Attentive Recurrent Network for Time-domain Multichannel Speech Enhancement

In this work, we propose a new model called triple-path attentive recurr...

0 Ashutosh Pandey, et al. ∙

research

∙ 10/19/2021

Continual self-training with bootstrapped remixing for speech enhancement

We propose RemixIT, a simple and novel self-supervised training method f...

1 Efthymios Tzinis, et al. ∙

research

∙ 10/14/2021

Conformer-Based Self-Supervised Learning for Non-Speech Audio Tasks

Representation learning from unlabeled data has been of major interest i...

0 Sangeeta Srivastava, et al. ∙

research

∙ 09/16/2021

NORESQA – A Framework for Speech Quality Assessment using Non-Matching References

The perceptual task of speech quality assessment (SQA) is a challenging ...

0 Pranay Manocha, et al. ∙

research

∙ 09/11/2021

Incorporating Real-world Noisy Speech in Neural-network-based Speech Enhancement Systems

Supervised speech enhancement relies on parallel databases of degraded s...

0 Yangyang Xia, et al. ∙

research

∙ 06/25/2021

Online Self-Attentive Gated RNNs for Real-Time Speaker Separation

Deep neural networks have recently shown great success in the task of bl...

0 Ori Kabeli, et al. ∙

research

∙ 06/21/2021

Do sound event representations generalize to other audio tasks? A case study in audio transfer learning

Transfer learning is critical for efficient information transfer across ...

0 Anurag Kumar, et al. ∙

research

∙ 05/29/2021

DPLM: A Deep Perceptual Spatial-Audio Localization Metric

Subjective evaluations are critical for assessing the perceptual realism...

0 Pranay Manocha, et al. ∙

research

∙ 05/24/2021

Decentralized, Hybrid MAC Design with Reduced State Information Exchange for Low-Delay IoT Applications

We consider a system of several collocated nodes sharing a time slotted ...

0 Avinash Mohan, et al. ∙

research

∙ 02/13/2021

Multi-Channel Speech Enhancement using Graph Neural Networks

Multi-channel speech enhancement aims to extract clean speech from a noi...

0 Panagiotis Tzirakis, et al. ∙

research

∙ 02/06/2021

A bandit approach to curriculum generation for automatic speech recognition

The Automated Speech Recognition (ASR) task has been a challenging domai...

0 Anastasia Kuznetsova, et al. ∙

research

∙ 12/05/2020

Implementing QZMAC (a Decentralized Delay Optimal MAC) over 6TiSCH under the Contiki OS in an IEEE 802.15.4 Network

Motivated by the emerging delay-sensitive applications of the Internet o...

0 Shivam Vinayak Vatsa, et al. ∙

research

∙ 09/02/2020

SAGRNN: Self-Attentive Gated RNN for Binaural Speaker Separation with Interaural Cue Preservation

Most existing deep learning based binaural speaker separation systems fo...

0 Ke Tan, et al. ∙

research

∙ 06/30/2020

A Sequential Self Teaching Approach for Improving Generalization in Sound Event Recognition

An important problem in machine auditory perception is to recognize and ...

0 Anurag Kumar, et al. ∙

research

∙ 05/29/2020

Large Scale Audiovisual Learning of Sounds with Weakly Labeled Data

Recognizing sounds is a key aspect of computational audio scene analysis...

8 Haytham M. Fayek, et al. ∙

research

∙ 02/19/2020

Throughput Optimal Decentralized Scheduling with Single-bit State Feedback for a Class of Queueing Systems

Motivated by medium access control for resource-challenged wireless Inte...

0 Avinash Mohan, et al. ∙

research

∙ 10/25/2019

SeCoST: Sequential Co-Supervision for Weakly Labeled Audio Event Detection

Weakly supervised learning algorithms are critical for scaling audio eve...

0 Anurag Kumar, et al. ∙

research

∙ 11/25/2018

Learning Sound Events From Webly Labeled Data

In the last couple of years, weakly labeled learning for sound events ha...

0 Anurag Kumar, et al. ∙

research

∙ 04/24/2018

A Closer Look at Weak Label Learning for Audio Events

Audio content analysis in terms of sound events is an important research...

0 Ankit Shah, et al. ∙

research

∙ 01/17/2018

NELS - Never-Ending Learner of Sounds

Sounds are essential to how humans perceive and interact with the world ...

0 Benjamin Elizalde, et al. ∙

research

∙ 11/04/2017

Knowledge Transfer from Weakly Labeled Audio using Convolutional Neural Network for Sound Events and Scenes

In this work we propose approaches to effectively transfer knowledge fro...

0 Anurag Kumar, et al. ∙

research

∙ 11/02/2017

Framework for evaluation of sound event detection in web videos

The largest source of sound events is web videos. Most videos lack sound...

0 Rohan Badlani, et al. ∙

research

∙ 10/30/2017

Content-based Representations of audio using Siamese neural networks

In this paper, we focus on the problem of content-based retrieval for au...

0 Pranay Manocha, et al. ∙

research

∙ 07/09/2017

Deep CNN Framework for Audio Event Recognition using Weakly Labeled Web Data

The development of audio event recognition models requires labeled train...

0 Anurag Kumar, et al. ∙

research

∙ 11/12/2016

Audio Event and Scene Recognition: A Unified Approach using Strongly and Weakly Labeled Data

In this paper we propose a novel learning framework called Supervised an...

0 Anurag Kumar, et al. ∙

research

∙ 09/23/2016

Discovering Sound Concepts and Acoustic Relations In Text

In this paper we describe approaches for discovering acoustic concepts a...

0 Anurag Kumar, et al. ∙

research

∙ 07/22/2016

Experiments on the DCASE Challenge 2016: Acoustic Scene Classification and Sound Event Detection in Real Life Recording

In this paper we present our work on Task 1 Acoustic Scene Classi- ficat...

0 Benjamin Elizalde, et al. ∙

research

∙ 07/09/2016

Classifier Risk Estimation under Limited Labeling Resources

In this paper we propose strategies for estimating performance of a clas...

0 Anurag Kumar, et al. ∙

research

∙ 02/06/2015

Unsupervised Fusion Weight Learning in Multiple Classifier Systems

In this paper we present an unsupervised method to learn the weights wit...

0 Anurag Kumar, et al. ∙

Anurag Kumar

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