A Dual-staged Context Aggregation Method Towards Efficient End-to-end Speech Enhancement
2019 · Kai Zhen, Mi Suk Lee, Minje Kim
Abstract
In speech enhancement, an end-to-end deep neural network converts a noisy speech signal to a clean speech directly in time domain without time-frequency transformation or mask estimation. However, aggregating contextual information from a high-resolution time domain signal with an affordable model complexity still remains challenging. In this paper, we propose a densely connected convolutional and recurrent network (DCCRN), a hybrid architecture, to enable dual-staged temporal context aggregation. With the dense connectivity and cross-component identical shortcut, DCCRN consistently outperforms competing convolutional baselines with an average STOI improvement of 0.23 and PESQ of 1.38 at three SNR levels. The proposed method is computationally efficient with only 1.38 million parameters. The generalizability performance on the unseen noise types is still decent considering its low complexity, although it is relatively weaker comparing to Wave-U-Net with 7.25 times more parameters.
Authors
(none)
Tags
Stats
Related papers
- Constrained Convolutional-recurrent Networks To Improve Speech Quality With Low Impact On Recognition Accuracy (2018)5.24
- DCCRN+: Channel-wise Subband DCCRN With SNR Estimation For Speech Enhancement (2021)0.00
- DCCRN: Deep Complex Convolution Recurrent Network For Phase-aware Speech Enhancement (2020)20.78
- Concatenated Identical DNN (CI-DNN) To Reduce Noise-type Dependence In Dnn-based Speech Enhancement (2018)5.24
- Multi-channel End-to-end Neural Network For Speech Enhancement, Source Localization, And Voice Activity Detection (2022)0.00
- Contextnet: Improving Convolutional Neural Networks For Automatic Speech Recognition With Global Context (2020)17.24
- DPCRN: Dual-path Convolution Recurrent Network For Single Channel Speech Enhancement (2021)14.35
- Real-time Monaural Speech Enhancement With Short-time Discrete Cosine Transform (2021)0.00