Zobrazeno 1 - 10
of 242
pro vyhledávání: '"Ostergaard, Jan"'
Room equalisation aims to increase the quality of loudspeaker reproduction in reverberant environments, compensating for colouration caused by imperfect room reflections and frequency dependant loudspeaker directivity. A common technique in the field
Externí odkaz:
http://arxiv.org/abs/2409.10131
The Effect of Training Dataset Size on Discriminative and Diffusion-Based Speech Enhancement Systems
Autor:
Gonzalez, Philippe, Tan, Zheng-Hua, Østergaard, Jan, Jensen, Jesper, Alstrøm, Tommy Sonne, May, Tobias
The performance of deep neural network-based speech enhancement systems typically increases with the training dataset size. However, studies that investigated the effect of training dataset size on speech enhancement performance did not consider rece
Externí odkaz:
http://arxiv.org/abs/2406.06160
Autor:
Østergaard, Jan
We present a new notion $R_\ell$ of higher-order common information, which quantifies the information that $\ell\geq 2$ arbitrarily distributed random variables have in common. We provide analytical lower bounds on $R_3$ and $R_4$ for jointly Gaussia
Externí odkaz:
http://arxiv.org/abs/2406.02001
Autor:
Østergaard, Jan
We quantify the average amount of redundant information that is transferred from a subset of relevant random source processes to a target process. To identify the relevant source processes, we consider those that are connected to the target process a
Externí odkaz:
http://arxiv.org/abs/2405.00368
Ensuring intelligible speech communication for hearing assistive devices in low-latency scenarios presents significant challenges in terms of speech enhancement, coding and transmission. In this paper, we propose novel solutions for low-latency joint
Externí odkaz:
http://arxiv.org/abs/2404.19375
Advanced auditory models are useful in designing signal-processing algorithms for hearing-loss compensation or speech enhancement. Such auditory models provide rich and detailed descriptions of the auditory pathway, and might allow for individualizat
Externí odkaz:
http://arxiv.org/abs/2403.10428
Autor:
Leer, Peter, Jensen, Jesper, Carney, Laurel H., Tan, Zheng-Hua, Østergaard, Jan, Bramsløw, Lars
This article investigates the use of deep neural networks (DNNs) for hearing-loss compensation. Hearing loss is a prevalent issue affecting millions of people worldwide, and conventional hearing aids have limitations in providing satisfactory compens
Externí odkaz:
http://arxiv.org/abs/2403.10420
In this paper, we propose the use of self-supervised pretraining on a large unlabelled data set to improve the performance of a personalized voice activity detection (VAD) model in adverse conditions. We pretrain a long short-term memory (LSTM)-encod
Externí odkaz:
http://arxiv.org/abs/2312.16613
Autor:
Gonzalez, Philippe, Tan, Zheng-Hua, Østergaard, Jan, Jensen, Jesper, Alstrøm, Tommy Sonne, May, Tobias
Diffusion models are a new class of generative models that have shown outstanding performance in image generation literature. As a consequence, studies have attempted to apply diffusion models to other tasks, such as speech enhancement. A popular app
Externí odkaz:
http://arxiv.org/abs/2312.04370
Autor:
Gonzalez, Philippe, Tan, Zheng-Hua, Østergaard, Jan, Jensen, Jesper, Alstrøm, Tommy Sonne, May, Tobias
Diffusion models are a new class of generative models that have recently been applied to speech enhancement successfully. Previous works have demonstrated their superior performance in mismatched conditions compared to state-of-the art discriminative
Externí odkaz:
http://arxiv.org/abs/2312.02683