Low-Complexity Multichannel Wiener Filtering Using Ambisonic Warping

Applying multichannel noise reduction in the spherical harmonic domain (SHD) can be computationally expensive. To reduce the complexity, an approach is proposed that combines signal-dependent beamforming with spatial warping for dimensionality reduction. In particular, the multichannel Wiener filter combined with spatial warping is investigated. Two different methods to combine beamforming with warping are investigated, both aiming to reduce the computational complexity of the multichannel Wiener filter, which can be mainly attributed to the power spectral density matrix inversions required to compute the beamforming weights. A performance evaluation using simulated reverberant spherical microphone array signals is conducted wherein it is shown that the proposed methods significantly reduce the computational complexity whilst providing high degrees of signal enhancement when compared to order-truncation of the SHD signal without warping.