A Constrained Maximum Likelihood Estimator of Speech and Noise Spectra with Application to Multi-Microphone Noise Reduction

Adel Zahedi, Michael Pedersen, Jan Østergaard, Lars Bramsløw, Thomas Christiansen, Jesper Jensen

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Abstrakt

One of the challenges with the implementation of multi-microphone noise reduction systems in practical applications lies in the need for the knowledge of the speech and noise covariance matrices. Recently, a method based on Maximum Likelihood (ML) estimation addressed this problem. Despite its relative success in practical setups, this method may suggest negative spectral components for the clean speech due to noise influences. In this paper, we suggest a new estimation technique that tackles this issue by enforcing a power constraint on the estimation problem. We compare the proposed method with the ML method both in synthetic and real-life scenarios using objective measures. The results suggest that the proposed method can improve speech quality without a loss of intelligibility.

OriginalsprogEngelsk
TitelICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Antal sider5
ForlagIEEE
Publikationsdatomaj 2020
Sider6944-6948
Artikelnummer9053077
ISBN (Trykt)978-1-5090-6632-2
ISBN (Elektronisk)978-1-5090-6631-5
DOI
StatusUdgivet - maj 2020
BegivenhedICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Barcelona, Spanien
Varighed: 4 maj 20208 maj 2020

Konference

KonferenceICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
LandSpanien
ByBarcelona
Periode04/05/202008/05/2020
NavnICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN1520-6149

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