Improving GANs for Speech Enhancement

By Huy P Phan, Ian Vince McLoughlin, Lam Dang Pham, Oliver Y. Chen, Philipp Koch, M. de Vos, Alfred Mertins. 2020

143 citations recorded by the source corpus.

Generative adversarial networks (GAN) have recently been shown to be efficient for speech enhancement. However, most, if not all, existing speech enhancement GANs (SEGAN) make use of a single generator to perform one-stage enhancement mapping. In this work, we propose to use multiple generators that are chained to perform multi-stage enhancement mapping, which gradually refines the noisy input signals in a stage-wise fashion. Furthermore, we study two scenarios: (1) the generators share their parameters and (2) the generators’ parameters are independent. The former constrains the generators to learn a common mapping that is iteratively applied at all enhancement stages and results in a small model footprint. On the contrary, the latter allows the generators to flexibly learn different enhancement mappings at different stages of the network at the cost of an increased model size. We demonstrate that the proposed multi-stage enhancement approach outperforms the one-stage SEGAN baseline, where the independent generators lead to more favorable results than the tied generators. The source code is available at http://github.com/pquochuy/idsegan.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

Real Time Speech Enhancement in the Waveform Domain (2020, background): “Over the last years, there has been a growing interest towards DNN based methods for speech enhancement [34, 35, 36, 37, 20, 38, 39, 22, 40, 7, 41, 8, 42, 21, 37].”
Conditional Diffusion Probabilistic Model for Speech Enhancement (2022, methodology): “We additionally compare CDiffuSE to other time-domain generative models, namely SEGAN [11], SE-Flow [15], and improved deep SEGAN (DSEGAN) [32].”
Universal Speech Enhancement with Score-based Diffusion (2022, unknown): “(Phan et al., 2020) 2.39 2.90 0.93 SE-Flow (Strauss & Edler, 2021) 2.43 3.09 DiffuSE (Lu et al., 2021) 2.44 3.03 CDiffuSE (Lu et al., 2022) 2.52 3.10 PR-WaveGlow (Maiti & Mandel, 2020) 3.10 0.91 CycleGAN-DCD (Yu et al., 2021) 2.90 3.49 0.94 PSMGAN (Routray & Mao, 2022) 2.92 3.52 PoCoNet (Isik et…”