Search Results for author:
Found 9 papers, 0 papers with code
Spatial-Temporal Activity-Informed Diarization and Separation
The global spatial activity functions are computed from the global spatial coherence functions based on frequency-averaged local spatial activity functions.
Learning-based Array Configuration-Independent Binaural Audio Telepresence with Scalable Signal Enhancement and Ambience Preservation
The results have shown that the proposed BAT system can achieve superior telepresence performance with the desired balance between signal enhancement and ambience preservation, even when the array configurations are unseen in the training phase.
Deep Beamforming for Speech Enhancement and Speaker Localization with an Array Response-Aware Loss Function
Experimental results have shown that the proposed deep beamformer, trained with the linearly weighted scale-invariant source-to-noise ratio (SI-SNR) and ARROW loss functions, achieves superior performance in speech enhancement and speaker localization compared to two baselines.
Array Configuration-Agnostic Personal Voice Activity Detection Based on Spatial Coherence
Personal voice activity detection has received increased attention due to the growing popularity of personal mobile devices and smart speakers.
Array Configuration-Agnostic Personalized Speech Enhancement using Long-Short-Term Spatial Coherence
Personalized speech enhancement has been a field of active research for suppression of speechlike interferers such as competing speakers or TV dialogues.
Model-matching Principle Applied to the Design of an Array-based All-neural Binaural Rendering System for Audio Telepresence
Telepresence aims to create an immersive but virtual experience of the audio and visual scene at the far end for users at the near end.
Multi-channel target speech enhancement based on ERB-scaled spatial coherence features
Recently, speech enhancement technologies that are based on deep learning have received considerable research attention.
Multi-channel end-to-end neural network for speech enhancement, source localization, and voice activity detection
In this study, a neural beamformer consisting of a beamformer and a novel multi-channel DCCRN is proposed for speech enhancement and source localization.
Learning-based personal speech enhancement for teleconferencing by exploiting spatial-spectral features
Furthermore, the proposed enhancement system was compared with a baseline system with speaker embeddings and interchannel phase difference.
