Zobrazeno 1 - 10
of 652
pro vyhledávání: '"Christensen Mads"'
Autor:
Christensen, Mads Bjerge
We study generating series encoding linking numbers between geodesics in arithmetic hyperbolic $3$-folds. We show that the series converge to functions on the genus $2$ Siegel upper-half plane and that certain explicit modifications have the transfor
Externí odkaz:
http://arxiv.org/abs/2410.17231
Sound zone control (SZC) implemented using static optimal filters is significantly affected by various perturbations in the acoustic environment, an important one being the fluctuation in the speed of sound, which is in turn influenced by changes in
Externí odkaz:
http://arxiv.org/abs/2410.07978
Autor:
Bhattacharjee, Sankha Subhra, Fuglsig, Andreas Jonas, Christensen, Flemming, Jensen, Jesper Rindom, Christensen, Mads Græsbøll
Performance of sound zone control (SZC) systems deployed in practical scenarios are highly sensitive to the location of the listener(s) and can degrade significantly when listener(s) are moving. This paper presents a robust SZC system that adapts to
Externí odkaz:
http://arxiv.org/abs/2410.07935
Autor:
Yu, Wenrui, Li, Qiongxiu, Lopuhaä-Zwakenberg, Milan, Christensen, Mads Græsbøll, Heusdens, Richard
Federated learning (FL) emerged as a paradigm designed to improve data privacy by enabling data to reside at its source, thus embedding privacy as a core consideration in FL architectures, whether centralized or decentralized. Contrasting with recent
Externí odkaz:
http://arxiv.org/abs/2407.09324
Publikováno v:
J. Chem. Phys. 159, 024123 (2023)
Global optimization of atomistic structure rely on the generation of new candidate structures in order to drive the exploration of the potential energy surface (PES) in search for the global minimum energy (GM) structure. In this work, we discuss a t
Externí odkaz:
http://arxiv.org/abs/2402.18338
In this work, we propose a frequency bin-wise method to estimate the single-channel speech presence probability (SPP) with multiple deep neural networks (DNNs) in the short-time Fourier transform domain. Since all frequency bins are typically conside
Externí odkaz:
http://arxiv.org/abs/2302.12048
This paper focuses on leveraging deep representation learning (DRL) for speech enhancement (SE). In general, the performance of the deep neural network (DNN) is heavily dependent on the learning of data representation. However, the DRL's importance i
Externí odkaz:
http://arxiv.org/abs/2211.09166
Autor:
Li, Qiongxiu, Gundersen, Jaron Skovsted, Tjell, Katrine, Wisniewski, Rafal, Christensen, Mads Græsbøll
Publikováno v:
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 4263-4267
Privacy has become a major concern in machine learning. In fact, the federated learning is motivated by the privacy concern as it does not allow to transmit the private data but only intermediate updates. However, federated learning does not always g
Externí odkaz:
http://arxiv.org/abs/2209.07833
In previous work, we proposed a variational autoencoder-based (VAE) Bayesian permutation training speech enhancement (SE) method (PVAE) which indicated that the SE performance of the traditional deep neural network-based (DNN) method could be improve
Externí odkaz:
http://arxiv.org/abs/2205.05581