arXiv subjects

Nico Gößling

Publications and source records attributed to Nico Gößling.

3 recordsLinked to original sources

arXiv2023

Computationally-efficient and perceptually-motivated rendering of diffuse reflections in room acoustics simulation

Geometrical acoustics is well suited for simulating room reverberation in interactive real-time applications. While the image source model (ISM) is exceptionally fast, the restriction to specular reflections impacts its perceptual plausibility. To account for diffuse late reverberation, hybrid approaches have been proposed, e.g., using a feedback delay network (FDN) in combination with the ISM. Here, a computationally-efficient, digital-filter approach is suggested to account for effects of non-specular reflections in the ISM and to couple scattered sound into a diffuse reverberation model using a spatially rendered FDN. Depending on the scattering coefficient of a room boundary, energy of each image source is split into a specular and a scattered part which is added to the diffuse sound field. Temporal effects as observed for an infinite ideal diffuse (Lambertian) reflector are simulated using cascaded all-pass filters. Effects of scattering and multiple (inter-) reflections caused by larger geometric disturbances at walls and by objects in the room are accounted for in a highly simplified manner. Using a single parameter to quantify deviations from an empty shoebox room, each reflection is temporally smeared using cascaded all-pass filters. The proposed method was perceptually evaluated against dummy head recordings of real rooms.

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arXiv2020

Binaural LCMV Beamforming with Partial Noise Estimation

Besides reducing undesired sources (interfering sources and background noise), another important objective of a binaural beamforming algorithm is to preserve the spatial impression of the acoustic scene, which can be achieved by preserving the binaural cues of all sound sources. While the binaural minimum variance distortionless response (BMVDR) beamformer provides a good noise reduction performance and preserves the binaural cues of the desired source, it does not allow to control the reduction of the interfering sources and distorts the binaural cues of the interfering sources and the background noise. Hence, several extensions have been proposed. First, the binaural linearly constrained minimum variance (BLCMV) beamformer uses additional constraints, enabling to control the reduction of the interfering sources while preserving their binaural cues. Second, the BMVDR with partial noise estimation (BMVDR-N) mixes the output signals of the BMVDR with the noisy reference microphone signals, enabling to control the binaural cues of the background noise. Merging the advantages of both extensions, in this paper we propose the BLCMV with partial noise estimation (BLCMV-N). We show that the output signals of the BLCMV-N can be interpreted as a mixture of the noisy reference microphone signals and the output signals of a BLCMV using an adjusted interference scaling parameter. We provide a theoretical comparison between the BMVDR, the BLCMV, the BMVDR-N and the proposed BLCMV-N in terms of noise and interference reduction performance and binaural cue preservation. Experimental results using recorded signals as well as the results of a perceptual listening test show that the BLCMV-N is able to preserve the binaural cues of an interfering source (like the BLCMV), while enabling to trade off between noise reduction performance and binaural cue preservation of the background noise (like the BMVDR-N).

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arXiv2019

RTF-steered binaural MVDR beamforming incorporating multiple external microphones

The binaural minimum-variance distortionless-response (BMVDR) beamformer is a well-known noise reduction algorithm that can be steered using the relative transfer function (RTF) vector of the desired speech source. Exploiting the availability of an external microphone that is spatially separated from the head-mounted microphones, an efficient method has been recently proposed to estimate the RTF vector in a diffuse noise field. When multiple external microphones are available, different RTF vector estimates can be obtained by using this method for each external microphone. In this paper, we propose several procedures to combine these RTF vector estimates, either by selecting the estimate corresponding to the highest input SNR, by averaging the estimates or by combining the estimates in order to maximize the output SNR of the BMVDR beamformer. Experimental results for a moving speaker and diffuse noise in a reverberant environment show that the output SNR-maximizing combination yields the largest binaural SNR improvement and also outperforms the state-of-the art covariance whitening method.

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