Adversarial multi-task learning with inverse mapping for speech enhancement

Qiu, Yuanhang; Wang, Ruili; Feng, Huanqing; Singh, Satwinder; Ma, Zhizhong; Jia, Xiaoyun

doi:10.1016/j.asoc.2022.108568

Cited by 9 publications

(4 citation statements)

References 15 publications

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“…Wang et al, 2020), the work of Kim et al (2021), J. Wu et al (2019), Ram et al (2020), Qiu et al (2022), Phan et al (2020), CMGAN (Cao et al, 2022), and VSEGAN (X. Xu et al, 2022) were released for enhancing speech and eliminate the noise from the speech signals.…”

Section: Speech Enhancementmentioning

confidence: 99%

“…For instance, C‐RNN‐GAN (Mogren, 2016) proposed to enhance the quality of music sounds. Likewise, SEGAN (Pascual et al, 2017), CP‐GAN (G. Liu, Gong, et al, 2020), SERGAN (Baby & Verhulst, 2019), cycle‐consistent GAN (Y. Wang et al, 2020), the work of Kim et al (2021), J. Wu et al (2019), Ram et al (2020), Qiu et al (2022), Phan et al (2020), CMGAN (Cao et al, 2022), and VSEGAN (X. Xu et al, 2022) were released for enhancing speech and eliminate the noise from the speech signals. Also, Vaw‐Gan (K. Zhou et al, 2021) proposed a framework for enhancing emotional speech conversion.…”

Section: Gan Applicationsmentioning

confidence: 99%

“…Gupta & Zou, 2019;Iqbal & Ali, 2018;Karbhari et al, 2021;Waheed et al, 2020), enhancing speech, and removing noisy signals (Baby & Verhulst, 2019;Kim et al, 2021;G. Liu, Gong, et al, 2020;Mogren, 2016;Pascual et al, 2017;Phan et al, 2020;Qiu et al, 2022;Ram et al, 2020;.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, GANs can be applied in various domains including enhancing the resolution of images (Karnewar & Wang, 2020; Y. Liu, Fan, et al, 2022; Odena et al, 2017; Shamsolmoali et al, 2019; Sun et al, 2021; L. Wang et al, 2018), completing the destroyed parts of images (Denton et al, 2016; Hassanpour et al, 2022; Jam et al, 2021; Kottler et al, 2022; H. Liu, Wan, et al, 2021), translating data between different domains (Babu & Dubey, 2021; Barzilay et al, 2021; Gao et al, 2020; Y. Liu, Chen, et al, 2021; Y. Li, Gan, et al, 2019; Nie et al, 2021; H. Park et al, 2018; Reed et al, 2016; J.‐Y. Zhu et al, 2017), predicting new behaviors of humans and expected poses (Barsoum et al, 2018; R. Cui et al, 2020; Q. Cui et al, 2021; Jain et al, 2020; L. Ma et al, 2021; Men et al, 2022; L. Tian et al, 2021; Yamane et al, 2020), generating synthetic data of a specific task (Chaudhari et al, 2019; A. Gupta & Zou, 2019; Iqbal & Ali, 2018; Karbhari et al, 2021; Waheed et al, 2020), enhancing speech, and removing noisy signals (Baby & Verhulst, 2019; Kim et al, 2021; G. Liu, Gong, et al, 2020; Mogren, 2016; Pascual et al, 2017; Phan et al, 2020; Qiu et al, 2022; Ram et al, 2020; Y. Wang et al, 2020; J. Wu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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A comprehensive review of generative adversarial networks: Fundamentals, applications, and challenges

Mohammed

2023

WIREs Computational Stats

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In machine learning, a generative model is responsible for generating new samples of data in terms of a probabilistic model. Generative adversarial network (GAN) has been widely used to generate realistic samples in different domains and outperforms its peers in the generative models family. However, producing a robust GAN model is not a trivial task because many challenges face the GAN during the training process and impact its performance, affecting the quality and diversity of the generated samples. In this article, we conduct a comprehensive review of GANs to present the fundamentals of GAN, including its components, types, and objective functions. Also, we present an overview of the evaluation matrices used to evaluate GAN models. Moreover, we list the applications of GANs and research work in various domains. Finally, we present the challenges that face GANs and highlight two significant issues, representing mode collapse and training instability, in addition to those research efforts that tackle these challenges.This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Deep Learning Statistical Learning and Exploratory Methods of the Data Sciences > Neural Networks

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