TaylorBeamformer: Learning All-Neural Beamformer for Multi-Channel Speech Enhancement from Taylor's Approximation Theory

TL;DR

TaylorBeamformer introduces a novel neural beamformer inspired by Taylor's approximation theory, decomposing the speech enhancement process into interpretable components and learning them end-to-end for improved performance.

Contribution

It proposes a new neural beamformer model based on Taylor's approximation, enhancing interpretability and performance in multi-channel speech enhancement.

Findings

Outperforms previous advanced baselines on LibriSpeech-based dataset.

Effectively decomposes the enhancement process into interpretable components.

Enables end-to-end training with trainable networks replacing derivatives.

Abstract

While existing end-to-end beamformers achieve impressive performance in various front-end speech processing tasks, they usually encapsulate the whole process into a black box and thus lack adequate interpretability. As an attempt to fill the blank, we propose a novel neural beamformer inspired by Taylor's approximation theory called TaylorBeamformer for multi-channel speech enhancement. The core idea is that the recovery process can be formulated as the spatial filtering in the neighborhood of the input mixture. Based on that, we decompose it into the superimposition of the 0th-order non-derivative and high-order derivative terms, where the former serves as the spatial filter and the latter is viewed as the residual noise canceller to further improve the speech quality. To enable end-to-end training, we replace the derivative operations with trainable networks and thus can learn from training data. Extensive experiments are conducted on the synthesized dataset based on LibriSpeech and results show that the proposed approach performs favorably against the previous advanced baselines.