17 citations · 36 across the 6 of their papers we have counts for
13 papers
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…
CURE Dataset: Ladder Networks for Audio Event Classification
Harishchandra Dubey, Dimitra Emmanouilidou, Ivan J. Tashev
Audio event classification is an important task for several applications such as surveillance, audio, video and multimedia retrieval etc. There are approximately 3M people with hea…
Predicting word error rate for reverberant speech
Hannes Gamper, Dimitra Emmanouilidou, Sebastian Braun +1
Reverberation negatively impacts the performance of automatic speech recognition (ASR). Prior work on quantifying the effect of reverberation has shown that clarity (C50), a parame…
Non-intrusive speech quality assessment using neural networks
Anderson R. Avila, Hannes Gamper, Chandan Reddy +3
Estimating the perceived quality of an audio signal is critical for many multimedia and audio processing systems. Providers strive to offer optimal and reliable services in order t…
Convolutional-Recurrent Neural Networks for Speech Enhancement
Han Zhao, Shuayb Zarar, Ivan Tashev +1
We propose an end-to-end model based on convolutional and recurrent neural networks for speech enhancement. Our model is purely data-driven and does not make any assumptions about…
Constrained Convolutional-Recurrent Networks to Improve Speech Quality with Low Impact on Recognition Accuracy
Rasool Fakoor, Xiaodong He, Ivan Tashev +1
For a speech-enhancement algorithm, it is highly desirable to simultaneously improve perceptual quality and recognition rate. Thanks to computational costs and model complexities,…
Precision Scaling of Neural Networks for Efficient Audio Processing
Jong Hwan Ko, Josh Fromm, Matthai Philipose +2
While deep neural networks have shown powerful performance in many audio applications, their large computation and memory demand has been a challenge for real-time processing. In t…
Reinforcement Learning To Adapt Speech Enhancement to Instantaneous Input Signal Quality
Rasool Fakoor, Xiaodong He, Ivan Tashev +1
Today, the optimal performance of existing noise-suppression algorithms, both data-driven and those based on classic statistical methods, is range bound to specific levels of insta…