Artificial bandwidth extension using deep neural networks for spectral envelope estimation

By Johannes Abel, Maximilian Strake, Tim Fingscheidt. 2016

26 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Oleg Rybakov
2. Marco Tagliasacchi
3. Yunpeng Li
4. Dominik Roblek
5. Victor Ungureanu

In Signal Processing

1. Marco Tagliasacchi
2. Oleg Rybakov
3. Yunpeng Li
4. Dominik Roblek
5. Neil Zeghidour
6. Shiliang Zhang
7. Zhihao Du
8. Kai Hu

Citations in the corpus

• Real-Time Speech Frequency Bandwidth Extension (2020, methodology): “Early methods were based on the source-filter model of speech production and exploit DNNs to estimate the upper frequency envelope [3].”