Visual-Informed Speech Enhancement Using Attention-Based Beamforming

Recent studies have demonstrated that incorporating auxiliary information, such as speaker voiceprint or visual cues, can substantially improve Speech Enhancement (SE) performance. However, single-channel methods often yield suboptimal results in low signal-to-noise ratio (SNR) conditions, when there is high reverberation, or in complex scenarios involving dynamic speakers, overlapping speech, or non-stationary noise. To address these issues, we propose a novel Visual-Informed Neural Beamforming Network (VI-NBFNet), which integrates microphone array signal processing and deep neural networks (DNNs) using multimodal input features. The proposed network leverages a pretrained visual speech recognition model to extract lip movements as input features, which serve for voice activity detection (VAD) and target speaker identification. The system is intended to handle both static and moving speakers by introducing a supervised end-to-end beamforming framework equipped with an attention mechanism. The experimental results demonstrated that the proposed audiovisual system has achieved better SE performance and robustness for both stationary and dynamic speaker scenarios, compared to several baseline methods.

Egonoise Resilient Source Localization and Speech Enhancement for Drones Using a Hybrid Model and Learning-Based Approach

Drones are becoming increasingly important in search and rescue missions, and even military operations. While the majority of drones are equipped with camera vision capabilities, the realm of drone audition remains underexplored due to the inherent challenge of mitigating the egonoise generated by the rotors. In this paper, we present a novel technique to address this extremely low signal-to-noise ratio (SNR) problem encountered by the microphone-embedded drones. The technique is implemented using a hybrid approach that combines Array Signal Processing (ASP) and Deep Neural Networks (DNN) to enhance the speech signals captured by a six-microphone uniform circular array mounted on a quadcopter. The system performs localization of the target speaker through beamsteering in conjunction with speech enhancement through a Generalized Sidelobe Canceller-DeepFilterNet 2 (GSC-DF2) system. To validate the system, the DREGON dataset and measured data are employed. Objective evaluations of the proposed hybrid approach demonstrated its superior performance over four baseline methods in the SNR condition as low as -30 dB.