43 citations · 46 across the 4 of their papers we have counts for
7 papers
DNN Quantization with Attention
Ghouthi Boukli Hacene, Lukas Mauch, Stefan Uhlich +1
Low-bit quantization of network weights and activations can drastically reduce the memory footprint, complexity, energy consumption and latency of Deep Neural Networks (DNNs). Howe…
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…
Mixed Precision DNNs: All you need is a good parametrization
Stefan Uhlich, Lukas Mauch, Fabien Cardinaux +5
Efficient deep neural network (DNN) inference on mobile or embedded devices typically involves quantization of the network parameters and activations. In particular, mixed precisio…
Iteratively Training Look-Up Tables for Network Quantization
Fabien Cardinaux, Stefan Uhlich, Kazuki Yoshiyama +4
Operating deep neural networks on devices with limited resources requires the reduction of their memory footprints and computational requirements. In this paper we introduce a trai…