# Blind and neural network-guided convolutional beamformer for joint denoising, dereverberation, and source separation

arXiv: https://arxiv.org/abs/2108.01836 · HTML: https://mlsanity.com/paper/2108.01836/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2021.

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

- [LAVSS: Location-Guided Audio-Visual Spatial Audio Separation](https://mlsanity.com/paper/2310.20446/) (2023)
- [Audio-visual End-to-end Multi-channel Speech Separation, Dereverberation and Recognition](https://mlsanity.com/paper/2307.02909/) (2023)

## Abstract (arXiv)

This paper proposes an approach for optimizing a Convolutional BeamFormer (CBF) that can jointly perform denoising (DN), dereverberation (DR), and source separation (SS). First, we develop a blind CBF optimization algorithm that requires no prior information on the sources or the room acoustics, by extending a conventional joint DR and SS method. For making the optimization computationally tractable, we incorporate two techniques into the approach: the Source-Wise Factorization (SW-Fact) of a CBF and the Independent Vector Extraction (IVE). To further improve the performance, we develop a method that integrates a neural network(NN) based source power spectra estimation with CBF optimization by an inverse-Gamma prior. Experiments using noisy reverberant mixtures reveal that our proposed method with both blind and NN-guided scenarios greatly outperforms the conventional state-of-the-art NN-supported mask-based CBF in terms of the improvement in automatic speech recognition and signal distortion reduction performance.