Zobrazeno 1 - 10
of 73
pro vyhledávání: '"Rosenkranz, Tobias"'
Multi-frame algorithms for single-channel speech enhancement are able to take advantage from short-time correlations within the speech signal. Deep Filtering (DF) was proposed to directly estimate a complex filter in frequency domain to take advantag
Externí odkaz:
http://arxiv.org/abs/2305.08227
Multi-frame algorithms for single-channel speech enhancement are able to take advantage from short-time correlations within the speech signal. Deep filtering (DF) recently demonstrated its capabilities for low-latency scenarios like hearing aids with
Externí odkaz:
http://arxiv.org/abs/2305.08225
Deep learning-based speech enhancement has seen huge improvements and recently also expanded to full band audio (48 kHz). However, many approaches have a rather high computational complexity and require big temporal buffers for real time usage e.g. d
Externí odkaz:
http://arxiv.org/abs/2205.05474
Complex-valued processing has brought deep learning-based speech enhancement and signal extraction to a new level. Typically, the process is based on a time-frequency (TF) mask which is applied to a noisy spectrogram, while complex masks (CM) are usu
Externí odkaz:
http://arxiv.org/abs/2110.05588
Complex-valued processing brought deep learning-based speech enhancement and signal extraction to a new level. Typically, the noise reduction process is based on a time-frequency (TF) mask which is applied to a noisy spectrogram. Complex masks (CM) u
Externí odkaz:
http://arxiv.org/abs/2006.13077
Autor:
Schröter, Hendrik, Rosenkranz, Tobias, Escalante-B., Alberto N., Zobel, Pascal, Maier, Andreas
Deep-learning based noise reduction algorithms have proven their success especially for non-stationary noises, which makes it desirable to also use them for embedded devices like hearing aids (HAs). This, however, is currently not possible with state
Externí odkaz:
http://arxiv.org/abs/2006.13067
Autor:
Schröter, Hendrik, Rosenkranz, Tobias, B., Alberto N. Escalante, Aubreville, Marc, Maier, Andreas
Noise reduction is an important part of modern hearing aids and is included in most commercially available devices. Deep learning-based state-of-the-art algorithms, however, either do not consider real-time and frequency resolution constrains or resu
Externí odkaz:
http://arxiv.org/abs/2001.10218
Autor:
Aubreville, Marc, Ehrensperger, Kai, Rosenkranz, Tobias, Graf, Benjamin, Puder, Henning, Maier, Andreas
Publikováno v:
2018 16th International Workshop on Acoustic Signal Enhancement (IWAENC), pp. 361-365
Reduction of unwanted environmental noises is an important feature of today's hearing aids (HA), which is why noise reduction is nowadays included in almost every commercially available device. The majority of these algorithms, however, is restricted
Externí odkaz:
http://arxiv.org/abs/1805.01198
Autor:
Banik, Niels, Braun, Stefan, Gerit Brandenburg, Jan, Fricker, Gert, Kalonia, Devendra S., Rosenkranz, Tobias
Publikováno v:
In International Journal of Pharmaceutics 15 October 2022 626
Publikováno v:
In Composites Part A September 2019 124