# Time-domain Speech Enhancement Assisted by Multi-resolution Frequency Encoder and Decoder

arXiv: https://arxiv.org/abs/2303.14593 · HTML: https://mlsanity.com/paper/2303.14593/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2023.

## Built on by (most cited first, 3 of 3)

- [Rethinking Mamba in Speech Processing by Self-Supervised Models](https://mlsanity.com/paper/2409.07273/) (2024)
- [Serialized Speech Information Guidance with Overlapped Encoding Separation for Multi-Speaker Automatic Speech Recognition](https://mlsanity.com/paper/2409.00815/) (2024)
- [Combining Deterministic Enhanced Conditions with Dual-Streaming Encoding for Diffusion-Based Speech Enhancement](https://mlsanity.com/paper/2505.13983/) (2025)

## Abstract (arXiv)

Time-domain speech enhancement (SE) has recently been intensively investigated. Among recent works, DEMUCS introduces multi-resolution STFT loss to enhance performance. However, some resolutions used for STFT contain non-stationary signals, and it is challenging to learn multi-resolution frequency losses simultaneously with only one output. For better use of multi-resolution frequency information, we supplement multiple spectrograms in different frame lengths into the time-domain encoders. They extract stationary frequency information in both narrowband and wideband. We also adopt multiple decoder outputs, each of which computes its corresponding resolution frequency loss. Experimental results show that (1) it is more effective to fuse stationary frequency features than non-stationary features in the encoder, and (2) the multiple outputs consistent with the frequency loss improve performance. Experiments on the Voice-Bank dataset show that the proposed method obtained a 0.14 PESQ improvement.