Search Results for author:
Found 8 papers, 4 papers with code
ICASSP 2022 Deep Noise Suppression Challenge
We open-source datasets and test sets for researchers to train their deep noise suppression models, as well as a subjective evaluation framework based on ITU-T P. 835 to rate and rank-order the challenge entries.
MusicNet: Compact Convolutional Neural Network for Real-time Background Music Detection
With the recent growth of remote work, online meetings often encounter challenging audio contexts such as background noise, music, and echo.
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.
PDP: A General Neural Framework for Learning SAT Solvers
There have been recent efforts for incorporating Graph Neural Network models for learning fully neural solvers for constraint satisfaction problems (CSP) and particularly Boolean satisfiability (SAT).
Machine Learning at Microsoft with ML .NET
Machine Learning is transitioning from an art and science into a technology available to every developer.
Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach
Recent efforts to combine Representation Learning with Formal Methods, commonly known as the Neuro-Symbolic Methods, have given rise to a new trend of applying rich neural architectures to solve classical combinatorial optimization problems.
PDP: A General Neural Framework for Learning Constraint Satisfaction Solvers
In this paper, we propose a generic neural framework for learning CSP solvers that can be described in terms of probabilistic inference and yet learn search strategies beyond greedy search.
