Optimal Binaural LCMV Beamforming in Complex Acoustic Scenarios: Theoretical and Practical Insights

Binaural beamforming algorithms for head-mounted assistive listening devices are crucial to improve speech quality and speech intelligibility in noisy environments, while maintaining the spatial impression of the acoustic scene. While the well-known BMVDR beamformer is able to preserve the binaural cues of one desired source, the BLCMV beamformer uses additional constraints to also preserve the binaural cues of interfering sources. In this paper, we provide theoretical and practical insights on how to optimally set the interference scaling parameters in the BLCMV beamformer for an arbitrary number of interfering sources. In addition, since in practice only a limited temporal observation interval is available to estimate all required beamformer quantities, we provide an experimental evaluation in a complex acoustic scenario using measured impulse responses from hearing aids in a cafeteria for different observation intervals. The results show that even rather short observation intervals are sufficient to achieve a decent noise reduction performance and that a proposed threshold on the optimal interference scaling parameters leads to smaller binaural cue errors in practice.Comment: To appear in Proc. IWAENC 201

Speech Signal Enhancement in Cocktail Party Scenarios by Deep Learning based Virtual Sensing of Head-Mounted Microphones

The cocktail party effect refers to the human sense of hearing’s ability to pay attention to a single conversation while filtering out all other background noise. To mimic this human hearing ability for people with hearing loss, scientists integrate beamforming algorithms into the signal processing path of hearing aids or implants’ audio processors. Although these algorithms’ performance strongly depends on the number and spatial arrangement of the microphones, most devices are equipped with a small number of microphones mounted close to each other on the audio processor housing. We measured and evaluated the impact of the number and spatial arrangement of hearing aid or head-mounted microphones on the performance of the established Minimum Variance Distortionless Response beamformer in cocktail party scenarios. The measurements revealed that the optimal microphone placement exploits monaural cues (pinna-effect), is close to the target signal, and creates a large distance spread due to its spatial arrangement. However, this microphone placement is impractical for hearing aid or implant users, as it includes microphone positions such as on the forehead. To overcome microphones’ placement at impractical positions, we propose a deep virtual sensing estimation of the corresponding audio signals. The results of objective measures and a subjective listening test with 20 participants showed that the virtually sensed microphone signals significantly improved the speech quality, especially in cocktail party scenarios with low signal-to-noise ratios. Subjective speech quality was assessed using a 3-alternative forced choice procedure to determine which of the presented speech mixtures was most pleasant to understand. Hearing aid and cochlear implant (CI) users might benefit from the presented approach using virtually sensed microphone signals, especially in noisy environments