# TaylorBeamixer: Learning Taylor-Inspired All-Neural Multi-Channel Speech Enhancement from Beam-Space Dictionary Perspective

arXiv: https://arxiv.org/abs/2211.12024 · HTML: https://mlsanity.com/paper/2211.12024/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Speech enhancement and noise suppression, one of 11 directions in Speech and audio language models.
- Builds on 14 of its 38 references that are in the map.
- First posted on arXiv 22 Nov 2022.

## Placed in

- Speech and audio language models / [Speech enhancement and noise suppression](https://mlsanity.com/speech/speech-enhancement-and-noise-suppression/)

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [DNSMOS: A Non-Intrusive Perceptual Objective Speech Quality metric to evaluate Noise Suppressors](https://mlsanity.com/paper/2010.15258/) (2020)
- [End-to-end Microphone Permutation and Number Invariant Multi-channel Speech Separation](https://mlsanity.com/paper/1910.14104/) (2019)
- [Statistical Speech Enhancement Based on Probabilistic Integration of Variational Autoencoder and Non-Negative Matrix Factorization](https://mlsanity.com/paper/1710.11439/) (2017)
- [ADL-MVDR: All deep learning MVDR beamformer for target speech separation](https://mlsanity.com/paper/2008.06994/) (2020)
- [Insights Into Deep Non-linear Filters for Improved Multi-channel Speech Enhancement](https://mlsanity.com/paper/2206.13310/) (2022)
- [Embedding and Beamforming: All-neural Causal Beamformer for Multichannel Speech Enhancement](https://mlsanity.com/paper/2109.00265/) (2021)
- [Enhancing End-to-End Multi-channel Speech Separation via Spatial Feature Learning](https://mlsanity.com/paper/2003.03927/) (2020)
- [L3DAS22 Challenge: Learning 3D Audio Sources in a Real Office Environment](https://mlsanity.com/paper/2202.10372/) (2022)
- [Taylor, Can You Hear Me Now? A Taylor-Unfolding Framework for Monaural Speech Enhancement](https://mlsanity.com/paper/2205.00206/) (2022)
- [Complex-valued Spatial Autoencoders for Multichannel Speech Enhancement](https://mlsanity.com/paper/2108.03130/) (2021)
- [All-neural beamformer for continuous speech separation](https://mlsanity.com/paper/2110.06428/) (2021)

## Abstract (arXiv)

Despite the promising performance of existing frame-wise all-neural beamformers in the speech enhancement field, it remains unclear what the underlying mechanism exists. In this paper, we revisit the beamforming behavior from the beam-space dictionary perspective and formulate it into the learning and mixing of different beam-space components. Based on that, we propose an all-neural beamformer called TaylorBM to simulate Taylor's series expansion operation in which the 0th-order term serves as a spatial filter to conduct the beam mixing, and several high-order terms are tasked with residual noise cancellation for post-processing. The whole system is devised to work in an end-to-end manner. Experiments are conducted on the spatialized LibriSpeech corpus and results show that the proposed approach outperforms existing advanced baselines in terms of evaluation metrics.