Subspace Hybrid Beamforming for Head-worn Microphone Arrays

Autor:	Hafezi, Sina, Moore, Alastair H., Guiraud, Pierre, Naylor, Patrick A., Donley, Jacob, Tourbabin, Vladimir, Lunner, Thomas
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	A two-stage multi-channel speech enhancement method is proposed which consists of a novel adaptive beamformer, Hybrid Minimum Variance Distortionless Response (MVDR), Isotropic-MVDR (Iso), and a novel multi-channel spectral Principal Components Analysis (PCA) denoising. In the first stage, the Hybrid-MVDR performs multiple MVDRs using a dictionary of pre-defined noise field models and picks the minimum-power outcome, which benefits from the robustness of signal-independent beamforming and the performance of adaptive beamforming. In the second stage, the outcomes of Hybrid and Iso are jointly used in a two-channel PCA-based denoising to remove the 'musical noise' produced by Hybrid beamformer. On a dataset of real 'cocktail-party' recordings with head-worn array, the proposed method outperforms the baseline superdirective beamformer in noise suppression (fwSegSNR, SDR, SIR, SAR) and speech intelligibility (STOI) with similar speech quality (PESQ) improvement. Comment: 5 pages, 4 figures, accepted for ICASSP 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2303.08967 Zobrazit plný text záznamu View this record from Arxiv