Zobrazeno 1 - 10
of 286
pro vyhledávání: '"Strauss, Martin"'
This paper proposes SEFGAN, a Deep Neural Network (DNN) combining maximum likelihood training and Generative Adversarial Networks (GANs) for efficient speech enhancement (SE). For this, a DNN is trained to synthesize the enhanced speech conditioned o
Externí odkaz:
http://arxiv.org/abs/2312.01744
Dialogue Enhancement (DE) enables the rebalancing of dialogue and background sounds to fit personal preferences and needs in the context of broadcast audio. When individual audio stems are unavailable from production, Dialogue Separation (DS) can be
Externí odkaz:
http://arxiv.org/abs/2305.19100
Deep generative models for Speech Enhancement (SE) received increasing attention in recent years. The most prominent example are Generative Adversarial Networks (GANs), while normalizing flows (NF) received less attention despite their potential. Bui
Externí odkaz:
http://arxiv.org/abs/2210.11654
Autor:
Strauss, Martin, Edler, Bernd
Speech enhancement involves the distinction of a target speech signal from an intrusive background. Although generative approaches using Variational Autoencoders or Generative Adversarial Networks (GANs) have increasingly been used in recent years, n
Externí odkaz:
http://arxiv.org/abs/2106.09008
This paper describes a hands-on comparison on using state-of-the-art music source separation deep neural networks (DNNs) before and after task-specific fine-tuning for separating speech content from non-speech content in broadcast audio (i.e., dialog
Externí odkaz:
http://arxiv.org/abs/2106.09093
Publikováno v:
IEEE Signal Processing Magazine, Institute of Electrical and Electronics Engineers, In press
Unmanned aerial vehicles (UAV), commonly referred to as drones, have raised increasing interest in recent years. Search and rescue scenarios where humans in emergency situations need to be quickly found in areas difficult to access constitute an impo
Externí odkaz:
http://arxiv.org/abs/1907.04655
Autor:
Bower, Amanda, Kitchen, Sarah N., Niss, Laura, Strauss, Martin J., Vargas, Alexander, Venkatasubramanian, Suresh
This work facilitates ensuring fairness of machine learning in the real world by decoupling fairness considerations in compound decisions. In particular, this work studies how fairness propagates through a compound decision-making processes, which we
Externí odkaz:
http://arxiv.org/abs/1707.00391
Syfte: Vårt syfte med denna studie är att försöka få en fördjupad förståelse av hur pedagoger ser på tidsaspekten som ett hinder för arbetet med pedagogisk dokumentation och vi är intresserade av hur pedagogisk dokumentation kan bli en int
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-35211
Publikováno v:
ACM Transactions on Algorithms, Vol. 13, No. 3, pp 32:1--32:26, 2017
An approximate sparse recovery system in $\ell_1$ norm consists of parameters $k$, $\epsilon$, $N$, an $m$-by-$N$ measurement $\Phi$, and a recovery algorithm, $\mathcal{R}$. Given a vector, $\mathbf{x}$, the system approximates $x$ by $\widehat{\mat
Externí odkaz:
http://arxiv.org/abs/1402.1726