# Cyclic Multichannel Wiener Filter for Acoustic Beamforming

arXiv: https://arxiv.org/abs/2507.10159 · HTML: https://mlsanity.com/paper/2507.10159/

## Key facts

- Cited by 1 papers in the map (data through 19 Oct 2026).
- First posted on arXiv 14 Jul 2025.

## Built on by (most cited first, 1 of 1)

- [A two-step approach for speech enhancement in low-SNR scenarios using cyclostationary beamforming and DNNs](https://mlsanity.com/paper/2602.12986/) (2026)

## Abstract (arXiv)

Acoustic beamforming models typically assume wide-sense stationarity of speech signals within short time frames. However, voiced speech is better modeled as a cyclostationary (CS) process, a random process whose mean and autocorrelation are T1-periodic, where α1=1/T1 corresponds to the fundamental frequency of vowels. Higher harmonic frequencies are found at integer multiples of the fundamental. This work introduces a cyclic multichannel Wiener filter (cMWF) for speech enhancement derived from a cyclostationary model. This beamformer exploits spectral correlation across the harmonic frequencies of the signal to further reduce the mean-squared error (MSE) between the target and the processed input. The proposed cMWF is optimal in the MSE sense and reduces to the MWF when the target is wide-sense stationary. Experiments on simulated data demonstrate considerable improvements in scale-invariant signal-to-distortion ratio (SI-SDR) on synthetic data but also indicate high sensitivity to the accuracy of the estimated fundamental frequency α1, which limits effectiveness on real data.