In noise reduction, a common approach is to use a microphone array with a beamformer that combines the individual microphone signals to extract a desired speech signal. The beamformer weights usually depend on the statistics of the noise and desired speech signals, which cannot be directly observed and must be estimated. Estimators based on the speech presence probability (SPP) seek to update the statistics estimates only when desired speech is known to be absent or present. However, they do not normally distinguish between desired and undesired speech sources. In this contribution, an algorithm is proposed to distinguish between these two types of sources using additional spatial information, by estimating a desired speech presence probability based on the combination of a multichannel SPP and a direction of arrival (DOA) based probability. The DOA-based probability is computed using DOA estimates for each time-frequency bin. The estimated statistics are then used to compute the weights of a spherical harmonic domain tradeoff beamformer, which achieves a balance between noise reduction and speech distortion. The performance evaluation demonstrates the effectiveness of the proposed approach at suppressing both background noise and spatially coherent noise. A number of audio examples and sample spectrograms are also provided.

In speech communication systems the received microphone signals are commonly degraded by reverberation and ambient noise that can decrease the fidelity and intelligibility of a desired speaker. Reverberation can be modeled as non-stationary diffuse sound which is not directly observable. In this work, we derive a multichannel Wiener filter in the spherical harmonic domain to reduce both reverberation and noise. The filter depends on the direction-of-arrival of the direct sound of the desired speaker and an interference power spectral density matrix for which an estimator is developed. The resulting informed spatial filter incorporates instantaneous information about the diffuseness of the sound field into the design of the filter. In addition, it is shown how the proposed filter relates to the well-known robust minimum variance distortionless response filter that is also used for comparison in the evaluation. Experimental results show that the proposed spatial filter provides a tradeoff between noise reduction and dereverberation depending on the diffuse sound PSD.

Most beamformers used for noise reduction rely on the accurate estimation of the second-order statistics of the noise, and in some cases, of the desired signal. Speech presence probability (SPP) based statistics estimators seek to update the estimates only when speech is absent/present, however, when used with a fixed a priori SPP, they cannot distinguish between a coherent desired source and coherent noise sources. We propose to distinguish between desired and noise sources by estimating the second-order statistics with a direction of arrival dependent a priori SPP, which we then use to compute the weights of a spherical harmonic domain minimum variance distortionless response filter.

In this letter, we derive an expression for the expected incoherent noise reduction factor of a spherical harmonic domain (SHD) tradeoff beamformer. The tradeoff beamformer attempts to reduce noise while minimizing speech distortion, and includes the minimum variance distortionless response (MVDR) and multichannel Wiener filters as special cases. For the open spherical microphone array, we find a number of analogies between the expressions for the expected noise reduction factor of the SHD and spatial domain MVDR beamformers. In an anechoic environment we find that the performance of the SHD MVDR beamformer with an open array depends almost entirely on the number of microphones, as in the spatial domain.

A method is proposed for simulating the sound pressure signals on a spherical microphone array in a reverberant enclosure. The method employs spherical harmonic decomposition and takes into account scattering from a solid sphere. An analysis shows that the error in the decomposition can be made arbitrarily small given a sufficient number of spherical harmonics.