43 citations · 52 across the 5 of their papers we have counts for
4 papers · 1 filter
Training Speech Enhancement Systems with Noisy Speech Datasets
Koichi Saito, Stefan Uhlich, Giorgio Fabbro +1
Recently, deep neural network (DNN)-based speech enhancement (SE) systems have been used with great success. During training, such systems require clean speech data - ideally, in l…
Unsupervised Cross-Domain Speech-to-Speech Conversion with Time-Frequency Consistency
Mohammad Asif Khan, Fabien Cardinaux, Stefan Uhlich +2
In recent years generative adversarial network (GAN) based models have been successfully applied for unsupervised speech-to-speech conversion.The rich compact harmonic view of the…
Exploring the Best Loss Function for DNN-Based Low-latency Speech Enhancement with Temporal Convolutional Networks
Yuichiro Koyama, Tyler Vuong, Stefan Uhlich +1
Recently, deep neural networks (DNNs) have been successfully used for speech enhancement, and DNN-based speech enhancement is becoming an attractive research area. While time-frequ…
Closing the Training/Inference Gap for Deep Attractor Networks
Cyril Cadoux, Stefan Uhlich, Marc Ferras +1
This paper improves the deep attractor network (DANet) approach by closing its gap between training and inference. During training, DANet relies on attractors, which are computed f…