Chargement en cours...
RESIDUAL RECURRENT NEURAL NETWORK FOR SPEECH ENHANCEMENT
Most current speech enhancement models use spectrogram features that require an expensive transformation and result in phase information loss. Previous work has overcome these issues by using convolutional networks to learn the temporal correlations across high-resolution waveforms. These models, ho...
Enregistré dans:
| Publié dans: | Proc IEEE Int Conf Acoust Speech Signal Process |
|---|---|
| Auteurs principaux: | , , , |
| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
2020
|
| Sujets: | |
| Accès en ligne: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7954533/ https://ncbi.nlm.nih.gov/pubmed/33716575 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/icassp40776.2020.9053544 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|