Exploiting time-frequency patterns with LSTM-RNNs for low-bitrate audio restoration
By Jun Deng, Björn Schuller, Florian Eyben, Dagmar M. Schuller, Zixing Zhang, Holly Francois, Eunmi L. Oh. 2019
39 citations recorded by the source corpus.
Subfields: 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
In Signal Processing
- Lars F. Villemoes
- Cong Zhou
- Janusz Klejsa
- D. Jia
- Arijit Biswas
- Roy Fejgin
- Jan Skoglund
- Jean-Marc Valin
Citations in the corpus
- Audio Codec Enhancement with Generative Adversarial Networks (2020, methodology): “develop backwards compatible methods for coded audio restoration [10]–[14].”
- Source Coding of Audio Signals with a Generative Model (2020, methodology): “Deep neural networks have already been applied to the audio coding problem in [10–12].”
- High Quality Audio Coding with Mdctnet (2022, methodology): “For general audio coding, the main tracks of research have been to improve the outputs of traditional codecs [5-10] or to train a neural net end-to-end as an autoencoder for regression according to a perceptually motivated loss function [11].”