Blind separation of the sounds in an Ambisonic sound scene is a challenging problem, especially when the spatial impression of these sounds needs to be preserved. In this work, we consider Ambisonic-to-Ambisonic separation of reverberant speech mixtures, optionally containing noise. A supervised learning approach is adopted utilizing a transformer-based deep neural network, denoted by AmbiSep. AmbiSep takes mutichannel Ambisonic signals as input and estimates separate multichannel Ambisonic signals for each speaker while preserving their spatial images including reverberation. The GPU memory requirement of AmbiSep during training increases with the number of Ambisonic channels. To overcome this issue, we propose different aggregation methods. The model is trained and evaluated for first-order and second-order Ambisonics using simulated speech mixtures. Experimental results show that the model performs well on clean and noisy reverberant speech mixtures, and also generalizes to mixtures generated with measured Ambisonic impulse responses.

Noise reduction in B-format recordings is particularly challenging as it concurrently requires to suppress undesired signals and preserve the spatial properties of the acoustic environment. In particular, wind noise poses an undesirable acoustic condition outdoors. In this work, methods to reduce wind noise while limiting the spatial distortions of the original signal are proposed based on recent works of the present authors. The main contributions are the derivation of the mentioned methods for B-format signals and the usage of the dipole-to-omnidirectional power ratio to control the trade-off between the desired-signal distortion and the noise reduction. The proposed methods are evaluated using B-format wind-noise recordings.

Ambisonics encodes the directional information of a sound field w.r.t. a reference position in an efficient and scalable way. However, the sound field might contain undesired noise. Reducing the noise while preserving the directional distribution of all sound field components is a challenging task. Recently, the present authors proposed a direction-preserving noise reduction method for higher-order Ambisonics (HOA) signals, which, in contrast to e.g. binaural beamforming methods, yields a HOA signal at the output. In this work, we investigate the direction-preserving noise reduction method further and compare it against a beamforming-based method and the matrix multi-channel Wiener filter. Different methods to estimate the power spectral densities which are needed for the noise reduction methods are discussed. Moreover, a method to preserve the reverberation of the desired signal is proposed. In the evaluation, the discussed methods are compared for different speech sources in anechoic and reverberant conditions and different noise types.

We present a spatial matrix filtering framework for noise reduction in the spherical harmonics (ambisonics) domain (SHD), which outputs an SHD signal vector rather than one signal as commonly provided by beamforming approaches. We discuss two spatial matrix filtering methods: A multi-beamformer method using known propagation vectors of the desired signal components and a method preserving the directional information in an optimal way. Parametric multi-channel Wiener filter solutions for both methods are discussed and a performance evaluation is conducted. It is shown that the direction preserving method preserves the spatial distribution of the desired sounds and residual noise at the cost of less noise reduction and higher signal distortion when compared to the multi-beamformer approach. Moreover, no spatial parameters have to be estimated.

Several techniques exist to estimate the directions of arrival (DOAs) of sound sources captured with a spherical microphone array. The eigenbeam rotational invariance technique (EB-ESPRIT) uses recurrence relations of spherical harmonics to estimate the DOAs. In this letter, we propose a new EB-ESPRIT method which uses three recurrence relations and a joint-diagonalization procedure to estimate the unit-vectors pointing to the source DOAs. We evaluate the angular estimation errors of the proposed method under noisy and reverberant conditions and compare it to existing EB-ESPRIT methods. We find that our proposed method can estimate the source DOAs with higher accuracy compared to the discussed existing EB-ESPRIT methods, and the estimation accuracy does not depend on the source DOAs.