Speech Denoising using Common Vector Analysis in Frequency Domain

Yıl 2016, Special Issue (2016), 224 - 229, 01.12.2016

Erol Seke , Mehmet Hakan Durak , Kemal Özkan

https://doi.org/10.18100/ijamec.270343

Öz

Signal denoising approaches on data of any dimension largely relies on
the assumption that data and the noise components and the noise itself are
somewhat uncorrelated. However, any denoising process heavily depending on this
assumption retreats when the signal component is adversely affected by the
operation. Therefore, several proposed algorithms try to separate the data into
two or more parts with varying noise levels so that denoising process can be
applied on them with different parameters and constraints. In this paper, the
proposed method separates the speech data into magnitude and phase where the
magnitude part is further separated into common and difference parts using
common vector analysis. It is assumed that the noise largely resides on difference
part and therefore denoised by a known algorithm. The speech data is
reconstructed by combining common, difference and phase parts. Using Linear
Minimum Mean Square Error Estimation algorithm on the difference part,
excellent denoising results are obtained. Results are compared with that of the
state of the art in well-known speech quality measures.

Anahtar Kelimeler

speech, denoising, Common Vector Analysis, CVA, subspace, frequency domain

Kaynakça

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