Signal Image and Video Processing· 2026Q2
GT-LFP: a progressive speech enhancement network with selective state space and dynamic frequency awareness
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- Q2SCImago
- 2026year
Short summary
A new progressive speech enhancement network, GT-LFP, achieves improved noise suppression and performance (PESQ, STOI) with a minimal parameter count (0.03M), making it suitable for real-time applications.
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Key points
- Introduces GT-LFP, a progressive speech enhancement network.
- Employs LSSE and FADC modules for enhanced complex noise suppression.
- Achieves improved PESQ and STOI scores compared to GTCRN.
- Maintains a very low model parameter count of 0.03M.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract Speech enhancement is a core issue in speech signal processing. It holds significant value in speech communication and speech recognition. This paper addresses the challenge of balancing model complexity and performance in deep learning-based speech enhancement. A Progressive Speech Enhancement Network, based on selective state space and dynamic frequency awareness, is proposed. The model uses a Selective State Space Enhancement (LSSE) module, a Frequency-Aware Dynamic Convolution (FADC) module, and a Progressive Mask Refinement (PMR) mechanism. These innovations enhance the suppression of complex noise. Experimental results demonstrate that the method enhances PESQ and STOI performance compared to the original GTCRN. It maintains a model parameter count of only 0.03 M. Thus, the method delivers strong speech enhancement and remains lightweight, making it suitable for real-time applications.
The authors' abstract, as published at the source. Signal Image and Video Processing, 2026 · DOI ↗
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Field: Signal Processing
Signal ProcessingComputer Science