Work

Simultaneous Speech Denoising and Super-Resolution Using mGLFB-Based U-Net, Fine-Tuned via Perceptual Loss

Year: 2025

Type: article

Abstract: This paper presents an efficient U-Net architecture featuring a modified Global Local Former Block (mGLFB) for simultaneous speech denoising and resolution reconstruction. Optimized for computational efficiency in the discrete cosine transform domain, the proposed architecture reduces model size by 13.5% compared to a standard GLFB-based U-Net, while maintaining comparable performance across multiple quality metrics. In addition to the mGLFB redesign, we introduce a perceptual loss that better captures high-frequency magnitude spectra, yielding notable gains in high-resolution recovery, especially in unvoiced speech segments. However, the mGLFB-based U-Net still shows limitations in retrieving spectral details with substantial energy in 4–6 kHz frequencies.

Source: Electronics

DOI: 10.3390/electronics14173466

Authors: Hwai-Tsu Hu, Hao-Hsuan Tsai

Institution: National Ilan University

Language: English

Topic: Speech and Audio Processing

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: National Science and Technology Council