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pro vyhledávání: '"Schröter, Hendrik"'
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
Autor:
Lai, Wei-Cheng, Schröter, Hendrik
The Ubicomp Digital 2020 -- Time Series Classification Challenge from STABILO is a challenge about multi-variate time series classification. The data collected from 100 volunteer writers, and contains 15 features measured with multiple sensors on a p
Externí odkaz:
http://arxiv.org/abs/2008.01078
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
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