no code implementations • 1 Nov 2022 • Yang Liu, Yangyang Shi, Yun Li, Kaustubh Kalgaonkar, Sriram Srinivasan, Xin Lei
End-to-End deep learning has shown promising results for speech enhancement tasks, such as noise suppression, dereverberation, and speech separation.
2 code implementations • 6 Jan 2021 • Chandan K A Reddy, Harishchandra Dubey, Kazuhito Koishida, Arun Nair, Vishak Gopal, Ross Cutler, Sebastian Braun, Hannes Gamper, Robert Aichner, Sriram Srinivasan
In this version of the challenge organized at INTERSPEECH 2021, we are expanding both our training and test datasets to accommodate full band scenarios.
no code implementations • 17 Dec 2020 • Chengyu Zheng, Xiulian Peng, Yuan Zhang, Sriram Srinivasan, Yan Lu
In this paper, we propose a novel idea to model speech and noise simultaneously in a two-branch convolutional neural network, namely SN-Net.
Ranked #1 on Speech Enhancement on Deep Noise Suppression (DNS) Challenge (PESQ-NB metric)
no code implementations • 23 Nov 2020 • Jayant Gupchup, Ashkan Aazami, Yaran Fan, Senja Filipi, Tom Finley, Scott Inglis, Marcus Asteborg, Luke Caroll, Rajan Chari, Markus Cozowicz, Vishak Gopal, Vinod Prakash, Sasikanth Bendapudi, Jack Gerrits, Eric Lau, Huazhou Liu, Marco Rossi, Dima Slobodianyk, Dmitri Birjukov, Matty Cooper, Nilesh Javar, Dmitriy Perednya, Sriram Srinivasan, John Langford, Ross Cutler, Johannes Gehrke
Large software systems tune hundreds of 'constants' to optimize their runtime performance.
1 code implementation • 10 Sep 2020 • Kusha Sridhar, Ross Cutler, Ando Saabas, Tanel Parnamaa, Hannes Gamper, Sebastian Braun, Robert Aichner, Sriram Srinivasan
In this challenge, we open source two large datasets to train AEC models under both single talk and double talk scenarios.
Acoustic echo cancellation Audio and Speech Processing Sound
1 code implementation • 16 May 2020 • Chandan K. A. Reddy, Vishak Gopal, Ross Cutler, Ebrahim Beyrami, Roger Cheng, Harishchandra Dubey, Sergiy Matusevych, Robert Aichner, Ashkan Aazami, Sebastian Braun, Puneet Rana, Sriram Srinivasan, Johannes Gehrke
In this challenge, we open-sourced a large clean speech and noise corpus for training the noise suppression models and a representative test set to real-world scenarios consisting of both synthetic and real recordings.
1 code implementation • 23 Jan 2020 • Chandan K. A. Reddy, Ebrahim Beyrami, Harishchandra Dubey, Vishak Gopal, Roger Cheng, Ross Cutler, Sergiy Matusevych, Robert Aichner, Ashkan Aazami, Sebastian Braun, Puneet Rana, Sriram Srinivasan, Johannes Gehrke
In this challenge, we open-source a large clean speech and noise corpus for training the noise suppression models and a representative test set to real-world scenarios consisting of both synthetic and real recordings.
no code implementations • 16 Jan 2020 • Varun Embar, Sriram Srinivasan, Lise Getoor
In this paper, we study the effectiveness of these approaches in estimating aggregate properties on networks with missing attributes.
no code implementations • 17 Sep 2019 • Chandan K. A. Reddy, Ebrahim Beyrami, Jamie Pool, Ross Cutler, Sriram Srinivasan, Johannes Gehrke
Our subjective MOS evaluation is the first large scale evaluation of Speech Enhancement algorithms that we are aware of.
14 code implementations • 26 Aug 2019 • Marc Lanctot, Edward Lockhart, Jean-Baptiste Lespiau, Vinicius Zambaldi, Satyaki Upadhyay, Julien Pérolat, Sriram Srinivasan, Finbarr Timbers, Karl Tuyls, Shayegan Omidshafiei, Daniel Hennes, Dustin Morrill, Paul Muller, Timo Ewalds, Ryan Faulkner, János Kramár, Bart De Vylder, Brennan Saeta, James Bradbury, David Ding, Sebastian Borgeaud, Matthew Lai, Julian Schrittwieser, Thomas Anthony, Edward Hughes, Ivo Danihelka, Jonah Ryan-Davis
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
1 code implementation • NeurIPS 2018 • Sriram Srinivasan, Marc Lanctot, Vinicius Zambaldi, Julien Perolat, Karl Tuyls, Remi Munos, Michael Bowling
Optimization of parameterized policies for reinforcement learning (RL) is an important and challenging problem in artificial intelligence.
1 code implementation • 7 Jul 2017 • Josh Merel, Yuval Tassa, Dhruva TB, Sriram Srinivasan, Jay Lemmon, Ziyu Wang, Greg Wayne, Nicolas Heess
Rapid progress in deep reinforcement learning has made it increasingly feasible to train controllers for high-dimensional humanoid bodies.
no code implementations • 19 May 2017 • Ehsan Amid, Manfred K. Warmuth, Sriram Srinivasan
We explain this by showing that $t_1 < 1$ caps the surrogate loss and $t_2 >1$ makes the predictive distribution have a heavy tail.
3 code implementations • ICML 2017 • Alexander Pritzel, Benigno Uria, Sriram Srinivasan, Adrià Puigdomènech, Oriol Vinyals, Demis Hassabis, Daan Wierstra, Charles Blundell
Deep reinforcement learning methods attain super-human performance in a wide range of environments.
1 code implementation • NeurIPS 2016 • Marc G. Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaul, David Saxton, Remi Munos
We consider an agent's uncertainty about its environment and the problem of generalizing this uncertainty across observations.
Ranked #7 on Atari Games on Atari 2600 Montezuma's Revenge
1 code implementation • 16 Apr 2016 • Parameswaran Raman, Sriram Srinivasan, Shin Matsushima, Xinhua Zhang, Hyokun Yun, S. V. N. Vishwanathan
Scaling multinomial logistic regression to datasets with very large number of data points and classes is challenging.
no code implementations • 16 Oct 2014 • Marlos C. Machado, Sriram Srinivasan, Michael Bowling
In Reinforcement Learning (RL), it is common to use optimistic initialization of value functions to encourage exploration.