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Found 8 papers, 5 papers with code
DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement
Multi-frame algorithms for single-channel speech enhancement are able to take advantage from short-time correlations within the speech signal.
Ranked #14 on
Speech Enhancement
on VoiceBank + DEMAND
Deep Multi-Frame Filtering for Hearing Aids
Alternatively, the complex filter can be estimated via an MF minimum variance distortionless response (MVDR), or MF Wiener filter (WF).
DeepFilterNet2: Towards Real-Time Speech Enhancement on Embedded Devices for Full-Band Audio
This makes the algorithm applicable to run on embedded devices in real-time.
DeepFilterNet: A Low Complexity Speech Enhancement Framework for Full-Band Audio based on Deep Filtering
In this work, we propose DeepFilterNet, a two stage speech enhancement framework utilizing deep filtering.
CLC: Complex Linear Coding for the DNS 2020 Challenge
Based on the published test set, we outperform the baseline w. r. t.
Measuring the Data Efficiency of Deep Learning Methods
The algorithms are trained on different-sized subsets of the MNIST and Omniglot data sets.
Improved graph-based SFA: Information preservation complements the slowness principle
The feature space spanned by HGSFA is complex due to the composition of the nonlinearities of the nodes in the network.
Theoretical Analysis of the Optimal Free Responses of Graph-Based SFA for the Design of Training Graphs
The method is versatile, directly supports multiple labels, and provides higher accuracy compared to current graphs for the problems considered.
