Zobrazeno 1 - 10
of 131
pro vyhledávání: '"Timo Gerkmann"'
Publikováno v:
IEEE Open Journal of Signal Processing, Vol 5, Pp 780-789 (2024)
In this work, we present a causal speech enhancement system that is designed to handle different types of corruptions. This paper is an extended version of our contribution to the “ICASSP 2023 Speech Signal Improvement Challenge”. The method is b
Externí odkaz:
https://doaj.org/article/1ee088978844472ea8e16e646f9c8b1f
Publikováno v:
EURASIP Journal on Audio, Speech, and Music Processing, Vol 2023, Iss 1, Pp 1-12 (2023)
Abstract A two-stage lightweight online dereverberation algorithm for hearing devices is presented in this paper. The approach combines a multi-channel multi-frame linear filter with a single-channel single-frame post-filter. Both components rely on
Externí odkaz:
https://doaj.org/article/54eb336fd76147918a6ed76361dd5b86
Publikováno v:
Frontiers in Robotics and AI, Vol 7 (2020)
Extracting information from noisy signals is of fundamental importance for both biological and artificial perceptual systems. To provide tractable solutions to this challenge, the fields of human perception and machine signal processing (SP) have dev
Externí odkaz:
https://doaj.org/article/bd97f52ceb13495a8af69380514ece3e
Autor:
Regina M. Baumgärtel, Martin Krawczyk-Becker, Daniel Marquardt, Christoph Völker, Hongmei Hu, Tobias Herzke, Graham Coleman, Kamil Adiloğlu, Stephan M. A. Ernst, Timo Gerkmann, Simon Doclo, Birger Kollmeier, Volker Hohmann, Mathias Dietz
Publikováno v:
Trends in Hearing, Vol 19 (2015)
In a collaborative research project, several monaural and binaural noise reduction algorithms have been comprehensively evaluated. In this article, eight selected noise reduction algorithms were assessed using instrumental measures, with a focus on t
Externí odkaz:
https://doaj.org/article/9ef09119a1ce4c15979c1b78dd8043ec
Autor:
Timo Gerkmann, Kristina Tesch
Publikováno v:
IEEE/ACM Transactions on Audio, Speech, and Language Processing. 31:563-575
The key advantage of using multiple microphones for speech enhancement is that spatial filtering can be used to complement the tempo-spectral processing. In a traditional setting, linear spatial filtering (beamforming) and single-channel post-filteri
Supervised masking approaches in the time-frequency domain aim to employ deep neural networks to estimate a multiplicative mask to extract clean speech. This leads to a single estimate for each input without any guarantees or measures of reliability.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1785b8d70744ef48e39d838f86355f15
Autor:
Huajian Fang, Timo Gerkmann
Single-channel deep speech enhancement approaches often estimate a single multiplicative mask to extract clean speech without a measure of its accuracy. Instead, in this work, we propose to quantify the uncertainty associated with clean speech estima
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7ad13857d1b4ce770dea3f058f7aeed0
http://arxiv.org/abs/2212.04831
http://arxiv.org/abs/2212.04831
Diffusion-based generative models have had a high impact on the computer vision and speech processing communities these past years. Besides data generation tasks, they have also been employed for data restoration tasks like speech enhancement and der
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6ef6397b8ee482ac84e55f7a8826db1f
http://arxiv.org/abs/2211.02397
http://arxiv.org/abs/2211.02397
To train machine learning algorithms to predict emotional expressions in terms of arousal and valence, annotated datasets are needed. However, as different people perceive others' emotional expressions differently, their annotations are subjective. T
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::352ec498dc5e99b406a621d87c788b16
https://doi.org/10.36227/techrxiv.21252693
https://doi.org/10.36227/techrxiv.21252693
Autor:
Bunlong Lay, Timo Gerkmann
Publikováno v:
2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP).