Real-life acoustic scenes may be recorded with microphone arrays for spatial audio applications, especially for the purpose of reproducing binaural signals for headphone listening. However, the presence of noise and interference may necessitate preprocessing to enhance the desired signal and improve the listener experience. Various methods have been developed to reduce noise while preserving the desired signal component with minimal distortion. The additional challenges posed by time-varying acoustic scenes are commonly addressed by segmenting the recorded signals into short time frames. Then, the short-time Fourier transform (STFT) is employed with multi-channel Wiener filter (MWF) and assuming the multiplicative transfer function (MTF) approximation. This approximation may not apply in the presence of long reverberation times and/or short STFT frames, so alternative techniques are required. This paper explores MWF-based enhancement in time-varying acoustic scenes where the MTF approximation is inapplicable, both analytically and experimentally with normal-hearing listeners. The investigated scene comprises a single desired source in a reverberant environment, and the impact of frame length and acoustic parameters on the rank of the spatial covariance matrix is studied. It is revealed that superior results in terms of reduced distortion and improved listener experience are achieved when using a full-rank spatial covariance matrix.

Telepresence for virtual meetings has gained interest due to recent travel limitations and the new reality of working from home. However, current literature supporting real-world microphone arrays for realistic telepresence in audio is very limited. This paper investigates a scenario of a distant participant joining virtually a meeting between two dynamic participants. The audio signal processing chain (i) starts by recording using an array mounted on glasses, (ii) with initial processing providing direction-of-arrival estimation of a desired speaker using a direct-path dominance test robust to reverberation, combined with speaker separation for improved dynamic localization, (iii) followed by speech enhancement against interfering speakers and noise, (iv) and ends with applying binaural signal matching for headphone listening. This paper compares model-based processing to learning-based processing in both noisy and dynamic scenarios, and presents a novel processing using data from a real wearable array, studied by simulation and a listening test.

In many applications, such as hearing aids and virtual reality, spatial audio is used to provide a more natural experience to the users. However, when captured in the real world, the audio signals may suffer from noise and interference. In this case, the challenge is to attenuate the undesired signals, while preserving the desired signals with their spatial information. In this paper, an approach for spatial signal enhancement is presented. This approach is based on two phases of estimation. The first phase is source signal estimation using a beamformer. Then, in the second phase, the acoustic transfer function (ATF) between the source and the array is estimated; this leads to an enhanced estimation of the desired signal at the microphones. This approach has been previously proposed, but was not investigated in depth. In this paper, a model for the estimated desired signals is developed. In contrast to other methods of spatial enhancement, no trade-off between noise reduction and signal distortion is found in this model, when there is a single desired source and a single interfering source in a reverberant room. To overcome the limited accuracy of ATF estimation for short duration signals, frequency smoothing is applied. Listening tests verify the performance of the proposed approach.

Speech enhancement in a single channel has been well studied in the literature in applications such as speech communication systems. However, in emerging applications such as virtual reality and spatial audio, in addition to attenuating undesired signals, the ability to preserve the spatial information of the desired signal captured in a noisy environment is of great importance. Nevertheless, there are only a few studies in the literature that propose solutions to this challenge. Most of these studies present solutions that attenuate the undesired signals, while preserving only limited spatial information regarding the desired signal, such as the direction of arrival (DOA). Methods that preserve complete spatial information have only recently been suggested, and have not been studied comprehensively. In this paper, two such methods based on time-frequency masking are investigated with the aim of attenuating the undesired signal, while preserving the spatial components of the desired signal. The first is referred to as spatial masking and is based on masking in the plane wave density (PWD) domain, and the second on masking in the spherical harmonics (SH) domain. The two methods are compared with a reference method, based on beamforming followed by single-channel time-frequency masking. Objective analysis and two listening tests were conducted in order to evaluate the performance of these methods for speech enhancement. It was shown that the spatial masking based method better preserves the desired component of the sound field, while the performance of the SH based method more strongly depends on the sources' distances. On the other hand, the SH based method better preserves the DOA of the residual noise, while the DOA of the residual noise under the spatial masking based method is strongly affected by the undesired signal.