# Low complexity online convolutional beamforming

arXiv: https://arxiv.org/abs/2107.06775 · HTML: https://mlsanity.com/paper/2107.06775/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2021.

## Built on by (most cited first, 2 of 2)

- [The Volcspeech system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge](https://mlsanity.com/paper/2202.04261/) (2022)
- [Neural Network-augmented Kalman Filtering for Robust Online Speech Dereverberation in Noisy Reverberant Environments](https://mlsanity.com/paper/2204.02741/) (2022)

## Abstract (arXiv)

Convolutional beamformers integrate the multichannel linear prediction model into beamformers, which provide good performance and optimality for joint dereverberation and noise reduction tasks. While longer filters are required to model long reverberation times, the computational burden of current online solutions grows fast with the filter length and number of microphones. In this work, we propose a low complexity convolutional beamformer using a Kalman filter derived affine projection algorithm to solve the adaptive filtering problem. The proposed solution is several orders of magnitude less complex than comparable existing solutions while slightly outperforming them on the REVERB challenge dataset.