15 citations · 35 across the 6 of their papers we have counts for
8 papers
DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement
Hendrik Schröter, Tobias Rosenkranz, Alberto N. Escalante-B. +1
Multi-frame algorithms for single-channel speech enhancement are able to take advantage from short-time correlations within the speech signal. Deep Filtering (DF) was proposed to d…
Deep Multi-Frame Filtering for Hearing Aids
Hendrik Schröter, Tobias Rosenkranz, Alberto N. Escalante-B. +1
Multi-frame algorithms for single-channel speech enhancement are able to take advantage from short-time correlations within the speech signal. Deep filtering (DF) recently demonstr…
DeepFilterNet2: Towards Real-Time Speech Enhancement on Embedded Devices for Full-Band Audio
Hendrik Schröter, Alberto N. Escalante-B., Tobias Rosenkranz +1
Deep learning-based speech enhancement has seen huge improvements and recently also expanded to full band audio (48 kHz). However, many approaches have a rather high computational…
CLC: Complex Linear Coding for the DNS 2020 Challenge
Hendrik Schröter, Tobias Rosenkranz, Alberto N. Escalante-B. +1
Complex-valued processing brought deep learning-based speech enhancement and signal extraction to a new level. Typically, the noise reduction process is based on a time-frequency (…
Lightweight Online Noise Reduction on Embedded Devices using Hierarchical Recurrent Neural Networks
Hendrik Schröter, Tobias Rosenkranz, Alberto N. Escalante-B. +2
Deep-learning based noise reduction algorithms have proven their success especially for non-stationary noises, which makes it desirable to also use them for embedded devices like h…
CLCNet: Deep learning-based Noise Reduction for Hearing Aids using Complex Linear Coding
Hendrik Schröter, Tobias Rosenkranz, Alberto N. Escalante B. +2
Noise reduction is an important part of modern hearing aids and is included in most commercially available devices. Deep learning-based state-of-the-art algorithms, however, either…
Measuring the Data Efficiency of Deep Learning Methods
Hlynur Davíð Hlynsson, Alberto N. Escalante-B., Laurenz Wiskott
In this paper, we propose a new experimental protocol and use it to benchmark the data efficiency --- performance as a function of training set size --- of two deep learning algori…
Theoretical Analysis of the Optimal Free Responses of Graph-Based SFA for the Design of Training Graphs
Alberto N. Escalante-B., Laurenz Wiskott
Slow feature analysis (SFA) is an unsupervised learning algorithm that extracts slowly varying features from a time series. Graph-based SFA (GSFA) is a supervised extension that ca…