# Deep Multi-Frame Filtering for Hearing Aids

arXiv: https://arxiv.org/abs/2305.08225 · HTML: https://mlsanity.com/paper/2305.08225/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2023.

## Built on by (most cited first, 4 of 4)

- [Music Enhancement with Deep Filters: A Technical Report for The ICASSP 2024 Cadenza Challenge](https://mlsanity.com/paper/2404.11116/) (2024)
- [Speech Boosting: Low-Latency Live Speech Enhancement for TWS Earbuds](https://mlsanity.com/paper/2409.18705/) (2024)
- [ConcateNet: Dialogue Separation Using Local And Global Feature Concatenation](https://mlsanity.com/paper/2408.08729/) (2024)
- [DFingerNet: Noise-Adaptive Speech Enhancement for Hearing Aids](https://mlsanity.com/paper/2501.10525/) (2025)

## Abstract (arXiv)

Multi-frame algorithms for single-channel speech enhancement are able to take advantage from short-time correlations within the speech signal. Deep filtering (DF) recently demonstrated its capabilities for low-latency scenarios like hearing aids with its complex multi-frame (MF) filter. Alternatively, the complex filter can be estimated via an MF minimum variance distortionless response (MVDR), or MF Wiener filter (WF). Previous studies have shown that incorporating algorithm domain knowledge using an MVDR filter might be beneficial compared to the direct filter estimation via DF. In this work, we compare the usage of various multi-frame filters such as DF, MF-MVDR, or MF-WF for HAs. We assess different covariance estimation methods for both MF-MVDR and MF-WF and objectively demonstrate an improved performance compared to direct DF estimation, significantly outperforming related work while improving the runtime performance.