Search Results for author:
Found 6 papers, 4 papers with code
Acoustic Echo Cancellation with Cross-Domain Learning
This paper proposes the Cross-Domain Echo-Controller(CDEC), submitted to the Interspeech 2021 AEC-Challenge. The algorithm consists of three building blocks: (i) a Time-Delay Compensation (TDC) module, (ii) a frequency-domainblock-based Acoustic Echo Canceler (AEC), and (iii) a Time-Domain Neural-Network (TD-NN) used as a post-processor. Our system achieves an overall MOS score of 3. 80, while onlyusing 2. 1 million parameters at a system latency of 32ms.
Blind Speech Separation and Dereverberation using Neural Beamforming
In this paper, we present the Blind Speech Separation and Dereverberation (BSSD) network, which performs simultaneous speaker separation, dereverberation and speaker identification in a single neural network.
Nonlinear Residual Echo Suppression using a Recurrent Neural Network
The acoustic front-end of hands-free communication de-vices introduces a variety of distortions to the linear echo pathbetween the loudspeaker and the microphone.
Resource-Efficient Speech Mask Estimation for Multi-Channel Speech Enhancement
While machine learning techniques are traditionally resource intensive, we are currently witnessing an increased interest in hardware and energy efficient approaches.
DEEP COMPLEX-VALUED NEURAL BEAMFORMERS
We propose a complex-valued deep neural network (cDNN) for speech enhancement and source separation.
Efficient and Robust Machine Learning for Real-World Systems
In that way, we provide an extensive overview of the current state-of-the-art of robust and efficient machine learning for real-world systems.
