Spark Deficient Gabor Frames for Inverse Problems
This work addresses signal processing challenges in speech denoising, but it appears incremental as it builds on existing Gabor transform methods with a specific modification.
The paper tackled the problem of improving signal denoising in inverse problems by applying a star-Digital Gabor Transform, which outperformed three state-of-the-art baseline Gabor transforms in all tested synthetic and real-world signal cases.
In this paper, we apply star-Digital Gabor Transform in analysis Compressed Sensing and speech denoising. Based on assumptions on the ambient dimension, we produce a window vector that generates a spark deficient Gabor frame with many linear dependencies among its elements. We conduct computational experiments on both synthetic and real-world signals, using as baseline three Gabor transforms generated by state-of-the-art window vectors and compare their performance to star-Gabor transform. Results show that the proposed star-Gabor transform outperforms all others in all signal cases.