I am a research lead and scientist at Meta Reality Labs Research. My broad research interests include Deep Learning, Audio/Speech Processing and Multimodal Learning. Often, my research focuses on weakly, self-supervised and unsupervised learning methods for different domains and problems.
Before joining Meta, I finished my PhD from School of Computer Science at Carnegie Mellon University in 2018. I was advised by Prof. Bhiksha Raj. My PhD thesis was Acoustic Intelligence in Machines, and it introduced weakly labeled learning of sounds, which has since played a crucial role in scaling sound event detection and classification. I obtained my undegraduate degree in Electrical Engineering from Indian Institute of Technology (IIT), Kanpur in 2013.
Some of my recent works have focused on Scene Understanding and Generation (audio-only and multimodal) [ CVPR-2023, arXiv-2023, CVPR-2022, IJCAI-2020, ICML-2020]; Speech Enhancement (single chanel, multi-channel, audio-visual) [ICASSP-2023, IEEE JSTSP-2022, ICASSP-2022, ICASSP-2021, ASRU-2021]; Deep Learning based Speech Assessment (Quality and Intelligibility) [ICASSP-2023, Interspeech-2022, Neurips-2021]. Check out my Google Scholar for a complete lists of my published works in various areas.
I regularly participate in different AI/Speech conferences (Neurips, ICML, ICASSP, Interspeech, ICLR, to mention a few) and journals (IEEE TASLP, IEEE SPL, IEEE TSP, Neural Networks, TMLR) in various roles - as Organizer/Reviewer/Program Committee Member/Guest Editor.
Google Scholar lists all of my publications.
‡ indicates equal contribution.
Torchaudio-Squim: Reference-Less Speech Quality and Intelligibility Measures in Torchaudio
Anurag Kumar, Ke Tan, Zhaoheng Ni, Pranay Manocha, Xiaohui Zhang, Ethan Henderson, Buye Xu
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023.
AV-NeRF: Learning Neural Fields for Real-World Audio-Visual Scene Synthesis
Susan Liang, Chao Huang, Yapeng Tian, Anurag Kumar, Chenliang Xu
Egocentric Audio-Visual Object Localization
Chao Huang, Yapeng Tian, Anurag Kumar, Chenliang Xu
IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023.
Remixit: Continual self-training of speech enhancement models via bootstrapped remixing
Efthymios Tzinis, Yossi Adi, Vamsi K Ithapu, Buye Xu, Paris Smaragdis, Anurag Kumar
IEEE Journal of Selected Topics in Signal Processing, 2022.
NORESQA--A Framework for Speech Quality Assessment using Non-Matching References
Pranay Manocha, Buye Xu, Anurag Kumar
Advances in neural information processing systems (Neurips), 2021.
Multi-Channel Speech Enhancement using Graph Neural Networks
Panagiotis Tzirakis, Anurag Kumar, Jacob Donley
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021.
A Sequential Self Teaching Approach for Improving Generalization in Sound Event Recognition
Anurag Kumar, Vamsi Krishna Ithapu
International Conference on Machine Learning (ICML), 2020.
Large Scale Audiovisual Learning of Sounds with Weakly Labeled Data
Haytham Fayek ‡, Anurag Kumar ‡
International Joint Conference on Artificial Intelligence (IJCAI), 2020.
Knowledge Transfer from Weakly Labeled Audio using Convolutional Neural Network for Sound Events and Scenes
Anurag Kumar, Maksim Khadkevich, Christian Fügen
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018.
Audio Event Detection using Weakly Labeled Data
Anurag Kumar, Bhiksha Raj
ACM International Conference on Multimedia (ACM MM), 2016.
Here is my CV (probably approximately correct!).