2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSPE-18.2
Paper Title JOINT MAXIMUM LIKELIHOOD ESTIMATION OF POWER SPECTRAL DENSITIES AND RELATIVE ACOUSTIC TRANSFER FUNCTIONS FOR ACOUSTIC BEAMFORMING
Authors Poul Hoang, Demant A/S, Denmark; Zheng-Hua Tan, Aalborg University, Denmark; Jan Mark de Han, Jesper Jensen, Demant A/S, Denmark
SessionSPE-18: Speech Enhancement 4: Multi-channel Processing
LocationGather.Town
Session Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Poster
Topic Speech Processing: [SPE-ENHA] Speech Enhancement and Separation
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Acoustic beamforming is crucial for many applications where extraction of a target signal from a noisy environment is required. In order to implement practical beamformers, e.g. the multichannel Wiener filter (MWF), estimation of the target and noise power spectral densities (PSDs), and the relative acoustic transfer functions (RATFs) is essential. Several methods, e.g. the so-called covariance whitening (CW) approach, have been proposed for estimating these parameters. However, it seems largely unknown that the CW approach in fact leads to maximum likelihood (ML) estimates of the RATFs. We use historical results to derive joint ML estimates (MLEs) of the RATFs and PSDs in the context of acoustic beamforming. In addition, based on the MLEs, we propose a basic VAD framework using concentrated likelihood ratios. We use the joint MLEs of the PSDs, RATFs, and the proposed VAD to implement beamformers in a hearing aid application, and compare its performance to competing methods. Simulation results show that the proposed scheme can outperform competing methods, in particular in realistic situations where highly accurate prior RATF knowledge is not available or at higher signal-to-noise ratios.