Real-Time Band-Grouped Vocal Denoising Using Sigmoid-Driven Ideal Ratio Masking
Fuente:
arXiv
Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908924486090752 |
|---|---|
| author | Williams, Daniel |
| author_facet | Williams, Daniel |
| contents | Real-time, deep learning-based vocal denoising has seen significant progress over the past few years, demonstrating the capability of artificial intelligence in preserving the naturalness of the voice while increasing the signal-to-noise ratio (SNR). However, many deep learning approaches have high amounts of latency and require long frames of context, making them difficult to configure for live applications. To address these challenges, we propose a sigmoid-driven ideal ratio mask trained with a spectral loss to encourage an increased SNR and maximized perceptual quality of the voice. The proposed model uses a band-grouped encoder-decoder architecture with frequency attention and achieves a total latency of less than 10,ms, with PESQ-WB improvements of 0.21 on stationary noise and 0.12 on nonstationary noise. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_29326 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Real-Time Band-Grouped Vocal Denoising Using Sigmoid-Driven Ideal Ratio Masking Williams, Daniel Sound Artificial Intelligence I.2.m; I.5.1 Real-time, deep learning-based vocal denoising has seen significant progress over the past few years, demonstrating the capability of artificial intelligence in preserving the naturalness of the voice while increasing the signal-to-noise ratio (SNR). However, many deep learning approaches have high amounts of latency and require long frames of context, making them difficult to configure for live applications. To address these challenges, we propose a sigmoid-driven ideal ratio mask trained with a spectral loss to encourage an increased SNR and maximized perceptual quality of the voice. The proposed model uses a band-grouped encoder-decoder architecture with frequency attention and achieves a total latency of less than 10,ms, with PESQ-WB improvements of 0.21 on stationary noise and 0.12 on nonstationary noise. |
| title | Real-Time Band-Grouped Vocal Denoising Using Sigmoid-Driven Ideal Ratio Masking |
| topic | Sound Artificial Intelligence I.2.m; I.5.1 |
| url | https://arxiv.org/abs/2603.29326 |