Search Results for author: Sergiy Matusevych

Found 8 papers, 4 papers with code

ICASSP 2022 Deep Noise Suppression Challenge

1 code implementation27 Feb 2022 Harishchandra Dubey, Vishak Gopal, Ross Cutler, Ashkan Aazami, Sergiy Matusevych, Sebastian Braun, Sefik Emre Eskimez, Manthan Thakker, Takuya Yoshioka, Hannes Gamper, Robert Aichner

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

no code implementations8 Oct 2021 Chandan K. A. Reddy, Vishak Gopa, Harishchandra Dubey, Sergiy Matusevych, Ross Cutler, Robert Aichner

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

1 code implementation16 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.

Speech Enhancement

The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Speech Quality and Testing Framework

1 code implementation23 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.

Speech Enhancement

PDP: A General Neural Framework for Learning SAT Solvers

no code implementations25 Sep 2019 Saeed Amizadeh, Sergiy Matusevych, Markus Weimer

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).

Learning To Solve Circuit-SAT: An Unsupervised Differentiable Approach

no code implementations ICLR 2019 Saeed Amizadeh, Sergiy Matusevych, Markus Weimer

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.

Combinatorial Optimization reinforcement-learning +1

PDP: A General Neural Framework for Learning Constraint Satisfaction Solvers

4 code implementations5 Mar 2019 Saeed Amizadeh, Sergiy Matusevych, Markus Weimer

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.

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