RNNoise-Ex: Hybrid Speech Enhancement System based on RNN and Spectral Features
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Abstract (arXiv)
Recent interest in exploiting Deep Learning techniques for Noise Suppression, has led to the creation of Hybrid Denoising Systems that combine classic Signal Processing with Deep Learning. In this paper, we concentrated our efforts on extending the RNNoise denoising system (arXiv:1709.08243) with the inclusion of complementary features during the training phase. We present a comprehensive explanation of the set-up process of a modified system and present the comparative results derived from a performance evaluation analysis, using a reference version of RNNoise as control.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 22 references that are in the map. First posted on arXiv 25 May 2021.
Builds on
5 of 22 references.
References · 5
- 2017Supervised Speech Separation Based on Deep Learning: An Overviewcited by 213
- 2017A Hybrid DSP/Deep Learning Approach to Real-Time Full-Band Speech Enhancementcited by 34
- 2017Exploring Speech Enhancement with Generative Adversarial Networks for Robust Speech Recognitioncited by 19
- 2016High-Quality, Low-Delay Music Coding in the Opus Codeccited by 12
- 2020A non-causal FFTNet architecture for speech enhancementcited by 3
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