A Kalman filtering algorithm with joint metrics-based tuning for single-channel speech enhancement

View/ Open
Author(s)
George, Aidan
So, Stephen
Ghosh, Ranadhir
Paliwal, Kuldip
Year published
2016
Metadata
Show full item recordAbstract
In this paper, we present an iterative Kalman filtering algorithm
that exhibits better speech enhancement by jointly utilising robustness
and sensitivity metrics. Typically, poor model parameter
estimates lead to a biased Kalman filter gain, which results
in innovation noise ‘leaking’ into the output. In the proposed
algorithm, the Kalman filter gain is dynamically tuned based on
a varying operating point of balanced robustness and sensitivity.
Speech enhancement experiments showed the proposed Kalman
filtering algorithm to produce higher quality speech than conventional
methods using objective and subjective measures.In this paper, we present an iterative Kalman filtering algorithm
that exhibits better speech enhancement by jointly utilising robustness
and sensitivity metrics. Typically, poor model parameter
estimates lead to a biased Kalman filter gain, which results
in innovation noise ‘leaking’ into the output. In the proposed
algorithm, the Kalman filter gain is dynamically tuned based on
a varying operating point of balanced robustness and sensitivity.
Speech enhancement experiments showed the proposed Kalman
filtering algorithm to produce higher quality speech than conventional
methods using objective and subjective measures.
View less >
View less >
Conference Title
Proceedings of the Sixteenth Australasian International Conference on Speech Science and Technology
Publisher URI
Copyright Statement
© 2016 ASSTA. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the conference's website for access to the definitive, published version.
Subject
Signal Processing