Speech enhancement by LSTM-based noise suppression followed by CNN-based speech restoration

Autor: Bruno Defraene, Tim Fingscheidt, Wouter Tirry, Maximilian Strake, Kristoff Fluyt

Publikováno v: EURASIP Journal on Advances in Signal Processing, Vol 2020, Iss 1, Pp 1-26 (2020)
EURASIP Journal on Advances in Signal Processing, 2020, 49 (2020). https://doi.org/10.1186/s13634-020-00707-1--http://asp.eurasipjournals.com/--http://www.bibliothek.uni-regensburg.de/ezeit/?2364203--1687-6180

Single-channel speech enhancement in highly non-stationary noise conditions is a very challenging task, especially when interfering speech is included in the noise. Deep learning-based approaches have notably improved the performance of speech enhanc

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e7a3be4309dc4a2b535f94890c37812
https://doaj.org/article/565f8fc0d3a94182b4106ab639dfc73d

Fully Convolutional Recurrent Networks for Speech Enhancement

Autor: Kristoff Fluyt, Tim Fingscheidt, Bruno Defraene, Maximilian Strake, Wouter Tirry

Publikováno v: ICASSP

Convolutional recurrent neural networks (CRNs) using convolutional encoder-decoder (CED) structures have shown promising performance for single-channel speech enhancement. These CRNs handle temporal modeling through integrating long short-term memory

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::290345c03e14fd2f340baa12a31527d8
https://doi.org/10.1109/icassp40776.2020.9054230