| Paper ID | AUD-26.4 |
| Paper Title |
COMPRESSED REPRESENTATION OF CEPSTRAL COEFFICIENTS VIA RECURRENT NEURAL NETWORKS FOR INFORMED SPEECH ENHANCEMENT |
| Authors |
Carol Chermaz, University of Edinburgh, United Kingdom; Dario Leuchtmann, Simon Tanner, Roger Wattenhofer, ETH Zurich, Switzerland |
| Session | AUD-26: Signal Enhancement and Restoration 3: Signal Enhancement |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 16:30 - 17:15 |
| Presentation Time: | Thursday, 10 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Audio and Acoustic Signal Processing: [AUD-SEN] Signal Enhancement and Restoration |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Speech enhancement is one of the biggest challenges in hearing prosthetics. In face-to-face communication devices have to estimate the signal of interest, but playback of speech signals from an electronic device opens up new opportunities. Audio signals can be enriched with hidden data, which can subsequently be decoded by the receiver. We investigate a hybrid strategy made of signal processing and RNN (Recurrent Neural Networks) to calculate and compress cepstral coefficients: these are descriptors of the speech signal, which can be embedded in the signal itself and used at the receiver's end to perform an Informed Speech Enhancement. Objective evaluations showed an increase in speech quality for noisy signals enhanced with our method. |
