A Dual-Staged Context Aggregation Method Towards Efficient End-To-End Speech Enhancement
Preprint
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.Keywords
All Related Versions
This publication has 9 references indexed in Scilit:
- Incorporating Symbolic Sequential Modeling for Speech Enhancement2019
- Gated Residual Networks With Dilated Convolutions for Monaural Speech EnhancementIEEE/ACM Transactions on Audio, Speech, and Language Processing, 2018
- End-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics Optimization by Fully Convolutional Neural NetworksIEEE/ACM Transactions on Audio, Speech, and Language Processing, 2018
- End-To-End Optimized Speech Coding with Deep Neural Networks2018
- SEGAN: Speech Enhancement Generative Adversarial Network2017
- Complex Ratio Masking for Monaural Speech SeparationIEEE/ACM Transactions on Audio, Speech, and Language Processing, 2015
- An Algorithm for Intelligibility Prediction of Time–Frequency Weighted Noisy SpeechIEEE Transactions on Audio, Speech, and Language Processing, 2011
- Performance measurement in blind audio source separationIEEE Transactions on Audio, Speech, and Language Processing, 2006
- Suppression of acoustic noise in speech using spectral subtractionIEEE Transactions on Acoustics, Speech, and Signal Processing, 1979