Search Results for author:
Found 17 papers, 10 papers with code
SCA: Streaming Cross-attention Alignment for Echo Cancellation
End-to-End deep learning has shown promising results for speech enhancement tasks, such as noise suppression, dereverberation, and speech separation.
Interspeech 2021 Deep Noise Suppression Challenge
In this version of the challenge organized at INTERSPEECH 2021, we are expanding both our training and test datasets to accommodate full band scenarios.
Interactive Speech and Noise Modeling for Speech Enhancement
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
(SI-SDR-NB metric)
Resonance: Replacing Software Constants with Context-Aware Models in Real-time Communication
Large software systems tune hundreds of 'constants' to optimize their runtime performance.
ICASSP 2021 Acoustic Echo Cancellation Challenge: Datasets and Testing Framework
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
The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results
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.
The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Speech Quality and Testing Framework
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.
Estimating Aggregate Properties In Relational Networks With Unobserved Data
In this paper, we study the effectiveness of these approaches in estimating aggregate properties on networks with missing attributes.
A scalable noisy speech dataset and online subjective test framework
Our subjective MOS evaluation is the first large scale evaluation of Speech Enhancement algorithms that we are aware of.
OpenSpiel: A Framework for Reinforcement Learning in Games
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
Actor-Critic Policy Optimization in Partially Observable Multiagent Environments
Optimization of parameterized policies for reinforcement learning (RL) is an important and challenging problem in artificial intelligence.
Learning human behaviors from motion capture by adversarial imitation
Rapid progress in deep reinforcement learning has made it increasingly feasible to train controllers for high-dimensional humanoid bodies.
Two-temperature logistic regression based on the Tsallis divergence
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.
Neural Episodic Control
Deep reinforcement learning methods attain super-human performance in a wide range of environments.
Unifying Count-Based Exploration and Intrinsic Motivation
We consider an agent's uncertainty about its environment and the problem of generalizing this uncertainty across observations.
Ranked #10 on
Atari Games
on Atari 2600 Montezuma's Revenge
DS-MLR: Exploiting Double Separability for Scaling up Distributed Multinomial Logistic Regression
Scaling multinomial logistic regression to datasets with very large number of data points and classes is challenging.
Domain-Independent Optimistic Initialization for Reinforcement Learning
In Reinforcement Learning (RL), it is common to use optimistic initialization of value functions to encourage exploration.
