Paper Info
Title
Dual-Channel Noise Reduction Based On A Mixture Of Circular-Symmetric Complex Gaussians On Unit Hypersphere
Abstract
In this paper a model-based dual-channel noise reduction approach is presented which is an alternative to conventional noise reduction algorithms essentially due to its independence of the noise power spectral density estimation and of any prior knowledge about the spatial noise field characteristics. We use a mixture of circular-symmetric complex-Gaussian distributions projected on the unit hypersphere for modeling the complex discrete Fourier transform coefficients of noisy speech signals in the frequency domain. According to the derived mixture model, clustering of the noise and the target speech components is performed depending on their direction of arrival. A soft masking strategy is proposed for speech enhancement based on responsibilities assigned to the target speech class in each time-frequency bin. Our experimental results show that the proposed approach is more robust than conventional dual-channel noise reduction systems based on the single-and dual-channel noise power spectral density estimators.
Year
DOI
Venue
2013
10.1109/ICASSP.2013.6639078
2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
Keywords
Field
DocType
Speech enhancement, dual-channel noise reduction, soft masking, mixture of Gaussians
Value noise,Noise power,Colors of noise,Pattern recognition,Noise measurement,Computer science,Phase noise,Artificial intelligence,Gaussian noise,Noise,Gradient noise
Conference
ISSN
Citations 
PageRank 
1520-6149
3
0.40
References 
Authors
9
4
Name
Order
Citations
PageRank
Jalal Taghia1515.36
Rainer Martin2102991.14
Jalil Taghia31178.82
Arne Leijon446124.91