127 citations · 263 across the 11 of their papers we have counts for
11 papers
On TasNet for Low-Latency Single-Speaker Speech Enhancement
Morten Kolbæk, Zheng-Hua Tan, Søren Holdt Jensen +1
In recent years, speech processing algorithms have seen tremendous progress primarily due to the deep learning renaissance. This is especially true for speech separation where the…
On Loss Functions for Supervised Monaural Time-Domain Speech Enhancement
Morten Kolbæk, Zheng-Hua Tan, Søren Holdt Jensen +1
Many deep learning-based speech enhancement algorithms are designed to minimize the mean-square error (MSE) in some transform domain between a predicted and a target speech signal.…
Source Coding in Networks with Covariance Distortion Constraints
Adel Zahedi, Jan Østergaard, Søren Holdt Jensen +2
We consider a source coding problem with a network scenario in mind, and formulate it as a remote vector Gaussian Wyner-Ziv problem under covariance matrix distortions. We define a…
Distributed Remote Vector Gaussian Source Coding for Wireless Acoustic Sensor Networks
Adel Zahedi, Jan Ostergaard, Soren Holdt Jensen +2
In this paper, we consider the problem of remote vector Gaussian source coding for a wireless acoustic sensor network. Each node receives messages from multiple nodes in the networ…
Distributed Remote Vector Gaussian Source Coding with Covariance Distortion Constraints
Adel Zahedi, Jan Ostergaard, Soren Holdt Jensen +2
In this paper, we consider a distributed remote source coding problem, where a sequence of observations of source vectors is available at the encoder. The problem is to specify the…
Compressive Time Delay Estimation Using Interpolation
Karsten Fyhn, Marco F. Duarte, Søren Holdt Jensen
Time delay estimation has long been an active area of research. In this work, we show that compressive sensing with interpolation may be used to achieve good estimation precision w…
Compressive Parameter Estimation for Sparse Translation-Invariant Signals Using Polar Interpolation
Karsten Fyhn, Marco F. Duarte, Søren Holdt Jensen
We propose new compressive parameter estimation algorithms that make use of polar interpolation to improve the estimator precision. Our work extends previous approaches involving p…
Compressive Sensing for Spread Spectrum Receivers
Karsten Fyhn, Tobias Lindstrøm Jensen, Torben Larsen +1
With the advent of ubiquitous computing there are two design parameters of wireless communication devices that become very important power: efficiency and production cost. Compress…
Demodulating Subsampled Direct Sequence Spread Spectrum Signals using Compressive Signal Processing
Karsten Fyhn, Thomas Arildsen, Torben Larsen +1
We show that to lower the sampling rate in a spread spectrum communication system using Direct Sequence Spread Spectrum (DSSS), compressive signal processing can be applied to demo…
Accelerated gradient methods for total-variation-based CT image reconstruction
Jakob Heide Jørgensen, Tobias Lindstrøm Jensen, Per Christian Hansen +3
Total-variation (TV)-based Computed Tomography (CT) image reconstruction has shown experimentally to be capable of producing accurate reconstructions from sparse-view data. In part…
Implementation of an Optimal First-Order Method for Strongly Convex Total Variation Regularization
Tobias Lindstrøm Jensen, Jakob Heide Jørgensen, Per Christian Hansen +1
We present a practical implementation of an optimal first-order method, due to Nesterov, for large-scale total variation regularization in tomographic reconstruction, image deblurr…