LORT: Locally Refined Convolution and Taylor Transformer for Monaural Speech Enhancement
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908563422576640 |
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| author | Wang, Junyu Lin, Zizhen Wang, Tianrui Ge, Meng Wang, Longbiao Dang, Jianwu |
| author_facet | Wang, Junyu Lin, Zizhen Wang, Tianrui Ge, Meng Wang, Longbiao Dang, Jianwu |
| contents | Achieving superior enhancement performance while maintaining a low parameter count and computational complexity remains a challenge in the field of speech enhancement. In this paper, we introduce LORT, a novel architecture that integrates spatial-channel enhanced Taylor Transformer and locally refined convolution for efficient and robust speech enhancement. We propose a Taylor multi-head self-attention (T-MSA) module enhanced with spatial-channel enhancement attention (SCEA), designed to facilitate inter-channel information exchange and alleviate the spatial attention limitations inherent in Taylor-based Transformers. To complement global modeling, we further present a locally refined convolution (LRC) block that integrates convolutional feed-forward layers, time-frequency dense local convolutions, and gated units to capture fine-grained local details. Built upon a U-Net-like encoder-decoder structure with only 16 output channels in the encoder, LORT processes noisy inputs through multi-resolution T-MSA modules using alternating downsampling and upsampling operations. The enhanced magnitude and phase spectra are decoded independently and optimized through a composite loss function that jointly considers magnitude, complex, phase, discriminator, and consistency objectives. Experimental results on the VCTK+DEMAND and DNS Challenge datasets demonstrate that LORT achieves competitive or superior performance to state-of-the-art (SOTA) models with only 0.96M parameters, highlighting its effectiveness for real-world speech enhancement applications with limited computational resources. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23832 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LORT: Locally Refined Convolution and Taylor Transformer for Monaural Speech Enhancement Wang, Junyu Lin, Zizhen Wang, Tianrui Ge, Meng Wang, Longbiao Dang, Jianwu Audio and Speech Processing Sound Achieving superior enhancement performance while maintaining a low parameter count and computational complexity remains a challenge in the field of speech enhancement. In this paper, we introduce LORT, a novel architecture that integrates spatial-channel enhanced Taylor Transformer and locally refined convolution for efficient and robust speech enhancement. We propose a Taylor multi-head self-attention (T-MSA) module enhanced with spatial-channel enhancement attention (SCEA), designed to facilitate inter-channel information exchange and alleviate the spatial attention limitations inherent in Taylor-based Transformers. To complement global modeling, we further present a locally refined convolution (LRC) block that integrates convolutional feed-forward layers, time-frequency dense local convolutions, and gated units to capture fine-grained local details. Built upon a U-Net-like encoder-decoder structure with only 16 output channels in the encoder, LORT processes noisy inputs through multi-resolution T-MSA modules using alternating downsampling and upsampling operations. The enhanced magnitude and phase spectra are decoded independently and optimized through a composite loss function that jointly considers magnitude, complex, phase, discriminator, and consistency objectives. Experimental results on the VCTK+DEMAND and DNS Challenge datasets demonstrate that LORT achieves competitive or superior performance to state-of-the-art (SOTA) models with only 0.96M parameters, highlighting its effectiveness for real-world speech enhancement applications with limited computational resources. |
| title | LORT: Locally Refined Convolution and Taylor Transformer for Monaural Speech Enhancement |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2509.23832 |