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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Statistical Beamformer Exploiting Non-stationarity and Sparsity with Spatially Constrained ICA for Robust Speech Recognition
In this paper, we present a statistical beamforming algorithm as a pre-processing step for robust automatic speech recognition (ASR). By modeling the target speech as a non-stationary Laplacian distribution, a mask-based…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Robust Speech Recognitionspeech-recognition+1Predictive Context-Awareness for Full-Immersive Multiuser Virtual Reality with Redirected Walking
The advancement of Virtual Reality (VR) technology is focused on improving its immersiveness, supporting multiuser Virtual Experiences (VEs), and enabling users to move freely within their VEs while remaining confined to…
Jailbreaking the Matrix: Nullspace Steering for Controlled Model Subversion
Large language models remain vulnerable to jailbreak attacks -- inputs designed to bypass safety mechanisms and elicit harmful responses -- despite advances in alignment and instruction tuning. We propose Head-Masked Nul…
Multi-Objective Distributed Beamforming Using High-Accuracy Synchronization and Localization
We present a multi-node, multi-objective open-loop microwave distributed beamforming system based on high-accuracy wireless synchronization and localization. Distributed beamforming requires accurate coordination of the …
Real-Time Object Tracking with On-Device Deep Learning for Adaptive Beamforming in Dynamic Acoustic Environments
Advances in object tracking and acoustic beamforming are driving new capabilities in surveillance, human-computer interaction, and robotics. This work presents an embedded system that integrates deep learning-based track…
Sound Source LocalizationDepth EstimationObject Tracking