A novel multi-speakers Urdu singing voices synthesizer using Wasserstein Generative Adversarial Network

Saeed, Ayesha; Hayat, Muhammad Faisal; Habib, Tufail; Ghaffar, Darakhshan Abdul; Qureshi, Muhammad Ali

doi:10.1016/j.specom.2021.12.005

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…In equation (11), W g and W f are two different convolutional filters, respectively. σ represents the sigmoid function.…”

Section: B Establishment Of a Digital Restoration Framework Based On ...mentioning

confidence: 99%

“…The research results showed that the WGANsing model had been further improved after model overshoot. This highlighted that the WGANsing model also had the ability to synthesize songs from languages other than English [11]. Zhang H et al proposed an attention generation adversarial network on the ground of contrastive learning for defect detection of colored patterned fabrics.…”

Section: Introductionmentioning

confidence: 99%

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Digital Restoration of Historical Buildings by Integrating 3D PC Reconstruction and GAN Algorithm

Fang,

Hui,

P. Rey

et al. 2024

JAIT

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Historical architecture is an important carrier of cultural and historical heritage in a country and region, and its protection and restoration work plays a crucial role in the inheritance of cultural heritage. However, the damage and destruction of buildings urgently need to be repaired due to the ancient age of historical buildings and the influence of natural environment and human factors. Therefore, an artificial intelligence repair technology based on three-dimensional (3D) point cloud reconstruction and generative adversarial networks was proposed to improve the precision and efficiency of repair work. Firstly, in-depth research on the principles and algorithms of 3D point cloud data processing and generative adversarial networks should be conducted. Secondly, a digital restoration framework was constructed by combining these two artificial intelligence technologies to achieve precise and efficient restoration of historical buildings through continuous adversarial learning processes. The experimental results showed that the errors in the restoration of palace buildings, defense walls, pagodas, altars, temples, and mausoleums were 0.17, 0.12, 0.13, 0.11, and 0.09, respectively. The technique can significantly reduce the error while maintaining the high precision repair effect. This technology with artificial intelligence as the core has excellent accuracy and stability in the digital restoration. It provides a new technical means for the digital restoration of historical buildings and has important practical significance for the protection of cultural heritage.

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“…In equation (11), W g and W f are two different convolutional filters, respectively. σ represents the sigmoid function.…”

Section: B Establishment Of a Digital Restoration Framework Based On ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Digital Restoration of Historical Buildings by Integrating 3D PC Reconstruction and GAN Algorithm

Fang,

Hui,

P. Rey

et al. 2024

JAIT

View full text Add to dashboard Cite

show abstract

Multi-Voice Singing Synthesis From Lyrics

Resna

Rajan

2022

Circuits Syst Signal Process

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A novel multi-speakers Urdu singing voices synthesizer using Wasserstein Generative Adversarial Network

Cited by 2 publications

References 16 publications

Digital Restoration of Historical Buildings by Integrating 3D PC Reconstruction and GAN Algorithm

Digital Restoration of Historical Buildings by Integrating 3D PC Reconstruction and GAN Algorithm

Multi-Voice Singing Synthesis From Lyrics

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