# Star DGT: a Robust Gabor Transform for Speech Denoising

arXiv: https://arxiv.org/abs/2104.14468 · HTML: https://mlsanity.com/paper/2104.14468/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2021.

## Built on by (most cited first, 2 of 2)

- [How to warm-start your unfolding network](https://mlsanity.com/paper/2502.01854/) (2025)
- [Frame the adversary: a structure-aware attack methodology](https://mlsanity.com/paper/2609.31128/) (2026)

## Abstract (arXiv)

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.