17 citations · 34 across the 4 of their papers we have counts for
9 papers · 1 filter
Low complexity online convolutional beamforming
Sebastian Braun, Ivan Tashev
Convolutional beamformers integrate the multichannel linear prediction model into beamformers, which provide good performance and optimality for joint dereverberation and noise red…
On training targets for noise-robust voice activity detection
Sebastian Braun, Ivan Tashev
The task of voice activity detection (VAD) is an often required module in various speech processing, analysis and classification tasks. While state-of-the-art neural network based…
Towards efficient models for real-time deep noise suppression
Sebastian Braun, Hannes Gamper, Chandan K. A. Reddy +1
With recent research advancements, deep learning models are becoming attractive and powerful choices for speech enhancement in real-time applications. While state-of-the-art models…
A consolidated view of loss functions for supervised deep learning-based speech enhancement
Sebastian Braun, Ivan Tashev
Deep learning-based speech enhancement for real-time applications recently made large advancements. Due to the lack of a tractable perceptual optimization target, many myths around…
Data augmentation and loss normalization for deep noise suppression
Sebastian Braun, Ivan Tashev
Speech enhancement using neural networks is recently receiving large attention in research and being integrated in commercial devices and applications. In this work, we investigate…
Weighted Speech Distortion Losses for Neural-network-based Real-time Speech Enhancement
Yangyang Xia, Sebastian Braun, Chandan K. A. Reddy +3
This paper investigates several aspects of training a RNN (recurrent neural network) that impact the objective and subjective quality of enhanced speech for real-time single-channe…