Multi-Domain Processing via Hybrid Denoising Networks for Speech Enhancement
arXiv:1812.08914
Abstract
We present a hybrid framework that leverages the trade-off between temporal and frequency precision in audio representations to improve the performance of speech enhancement task. We first show that conventional approaches using specific representations such as raw-audio and spectrograms are each effective at targeting different types of noise. By integrating both approaches, our model can learn multi-scale and multi-domain features, effectively removing noise existing on different regions on the time-frequency space in a complementary way. Experimental results show that the proposed hybrid model yields better performance and robustness than using each model individually.
arXiv comment7pages, 6 figures, 2 tables
References in corpus (1)
Cited by in corpus (7)
- CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement
- Exploring the Best Loss Function for DNN-Based Low-latency Speech Enhancement with Temporal Convolutional Networks
- PHASEN: A Phase-and-Harmonics-Aware Speech Enhancement Network
- Improving noise robust automatic speech recognition with single-channel time-domain enhancement network
- Dynamic Attention Based Generative Adversarial Network with Phase Post-Processing for Speech Enhancement
- Investigating Cross-Domain Losses for Speech Enhancement
- Speech Enhancement using Separable Polling Attention and Global Layer Normalization followed with PReLU