필터뱅크 기반 프로스트 알고리즘을 이용한 빔포밍 최적화Robust Speaker Identification Using Linear Transformation Optimized for Diagonal Covariance GMM

Beamforming Optimization Using Filterbank-based Frost AlgorithmJi-Hoon Park, Sung-Joo Lee, Jeong-Pyo Hong, Sang-Bae Jeong, Min-Soo HahnBeamforming is one of the spatial filtering techniques which extract only desired signals from noisy environments using microphone arrays. Fixed beamforming is a simple concept and easy to implement. However, it does not show good performance in real noisy conditions. As an adaptive beamforming, Frost algorithm can be a good candidate. It uses the concept of the linearly constrained minimum variance (LCMV) algorithm. The difference between the Frost and the LCMV algorithm is the error correction scheme which is very effective feature in the aspect of performance. In this paper, as quadrature mirror filtering (QMF)-based filterbank is utilized as the pre-processing of the Frost beamforming, the filter length and the learning rate of each band is optimized to improve the performance. The performance is measured by the signal-to-noise ratio (SNR) and the Barks scale spectral distortion (BSD).
Publisher
대한음성학회
Issue Date
2008-06
Language
KOR
Citation

말소리, v.1, no.66, pp.73 - 86

ISSN
1226-1173
URI
http://hdl.handle.net/10203/15384
Appears in Collection
EE-Journal Papers(저널논문)
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