Blar i NTNU Open på forfatter "Gelderblom, Femke B."
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Deep Complex Convolutional Recurrent Network for Multi-Channel Speech Enhancement and Dereverberation
Gelderblom, Femke B.; Myrvoll, Tor Andre (Chapter, 2021)This paper proposes a neural network based system for multi-channel speech enhancement and dereverberation. Speech recorded indoors by a far field microphone, is invariably degraded by noise and reflections. Recent single ... -
Evaluating Performance Metrics for Deep Neural Network-based Speech Enhancement Systems
Gelderblom, Femke B. (Doctoral theses at NTNU;2023:53, Doctoral thesis, 2023)A recurring challenge for speech enhancement (SE) systems, is that removing/reducing noise and reverberance does not necessarily increase the intelligibility or the quality of the speech for human listeners. Deep neural ... -
Subjective evaluation of a noise-reduced training target for deep neural network-based speech enhancement
Gelderblom, Femke B.; Tronstad, Tron Vedul; Viggen, Erlend Magnus (Journal article; Peer reviewed, 2018)Speech enhancement systems aim to improve the quality and intelligibility of noisy speech. In this study, we compare two speech enhancement systems based on deep neural networks. The speech intelligibility and quality of ... -
Subjective Intelligibility of Deep Neural Network-Based Speech Enhancement
Gelderblom, Femke B.; Tronstad, Tron Vedul; Viggen, Erlend Magnus (Journal article; Peer reviewed, 2017)Recent literature indicates increasing interest in deep neural networks for use in speech enhancement systems. Currently, these systems are mostly evaluated through objective measures of speech quality and/or intelligibility. ... -
SYNTHETIC DATA FOR DNN-BASED DOA ESTIMATION OF INDOOR SPEECH
Gelderblom, Femke B.; Kvam, Johannes; Liu, Yi; Myrvoll, Tor Andre (Chapter, 2021)This paper investigates the use of different room impulse response (RIR) simulation methods for synthesizing training data for deep neural network-based direction of arrival (DOA) estimation of speech in reverberant rooms. ...