IANS: Intelligibility-aware Null-steering Beamforming for Dual-Microphone Arrays

Beamforming techniques are popular in speech-related applications due to their effective spatial filtering capabilities. Nonetheless, conventional beamforming techniques generally depend heavily on either the target's direction-of-arrival (DOA), relative transfer function (RTF) or covariance matrix. This paper presents a new approach, the intelligibility-aware null-steering (IANS) beamforming framework, which uses the STOI-Net intelligibility prediction model to improve speech intelligibility without prior knowledge of the speech signal parameters mentioned earlier. The IANS framework combines a null-steering beamformer (NSBF) to generate a set of beamformed outputs, and STOI-Net, to determine the optimal result. Experimental results indicate that IANS can produce intelligibility-enhanced signals using a small dual-microphone array. The results are comparable to those obtained by null-steering beamformers with given knowledge of DOAs.

Speech Enhancement Based on Cyclegan with Noise-informed Training

Speech enhancement (SE) approaches can be classified into supervised and unsupervised categories. For unsupervised SE, a well-known cycle-consistent generative adversarial network (CycleGAN) model, which comprises two generators and two discriminators, has been shown to provide a powerful nonlinear mapping ability and thus achieve a promising noise-suppression capability. However, a low-efficiency training process along with insufficient knowledge between noisy and clean speech may limit the enhancement performance of the CycleGAN SE at runtime. In this study, we propose a novel noise-informed-training CycleGAN approach that incorporates additional inputs into the generators and discriminators to assist the CycleGAN in learning a more accurate transformation of speech signals between the noise and clean domains. The additional input feature serves as an indicator that provides more information during the CycleGAN training stage. Experiment results confirm that the proposed approach can improve the CycleGAN SE model while achieving a better sound quality and fewer signal distortions.