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arXiv · 2104.14468

Star DGT: a Robust Gabor Transform for Speech Denoising

Vasiliki Kouni·Holger Rauhut·Theoharis Theoharis
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Abstract

In this paper, we address the speech denoising problem, where Gaussian, pink and blue additive noises are to be removed from a given speech signal. Our approach is based on a redundant, analysis-sparse representation of the original speech signal. We pick an eigenvector of the Zauner unitary matrix and -- under certain assumptions on the ambient dimension -- we use it as window vector to generate a spark deficient Gabor frame. The analysis operator associated with such a frame, is a (highly) redundant Gabor transform, which we use as a sparsifying transform in denoising procedure. We conduct computational experiments on real-world speech data, using as baseline three Gabor transforms generated by state-of-the-art window vectors in time-frequency analysis and compare their performance to the proposed Gabor transform. The results show that our proposed redundant Gabor transform outperforms all others, consistently for all signals.

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Vasiliki Kouni, Holger Rauhut, Theoharis Theoharis. 2021-04-29. Star DGT: a Robust Gabor Transform for Speech Denoising. https://doi.org/10.1007/s43670-023-00053-x

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