# A two-step backward compatible fullband speech enhancement system

arXiv: https://arxiv.org/abs/2201.10809 · HTML: https://mlsanity.com/paper/2201.10809/

## Key facts

- Cited by 3 papers in the map (data through 19 Oct 2026).
- First posted on arXiv 26 Jan 2022.

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

- [Music Source Separation with Band-split RNN](https://mlsanity.com/paper/2209.15174/) (2022)
- [Harmonic enhancement using learnable comb filter for light-weight full-band speech enhancement model](https://mlsanity.com/paper/2306.00812/) (2023)
- [Optimizing Shoulder to Shoulder: A Coordinated Sub-Band Fusion Model for Real-Time Full-Band Speech Enhancement](https://mlsanity.com/paper/2203.16033/) (2022)

## Abstract (arXiv)

Speech enhancement methods based on deep learning have surpassed traditional methods. While many of these new approaches are operating on the wideband (16kHz) sample rate, a new fullband (48kHz) speech enhancement system is proposed in this paper. Compared to the existing fullband systems that utilizes perceptually motivated features to train the fullband speech enhancement using a single network structure, the proposed system is a two-step system ensuring good fullband speech enhancement quality while backward compatible to the existing wideband systems.