Quantum Fourier Transform Based Denoising: Unitary Filtering for Enhanced Speech Clarity

This paper introduces a quantum-inspired denoising framework that integrates the Quantum Fourier Transform (QFT) into classical audio enhancement pipelines. Unlike conventional Fast Fourier Transform (FFT) based methods, QFT provides a unitary transformation with global phase coherence and energy preservation, enabling improved discrimination between speech and noise. The proposed approach replaces FFT in Wiener and spectral subtraction filters with a QFT operator, ensuring consistent hyperparameter settings for fair comparison. Experiments on clean speech, synthetic tones, and noisy mixtures across diverse signal to noise ratio (SNR) conditions, demonstrate statistically significant gains in SNR, with up to 15 dB improvement and reduced artifact generation. Results confirm that QFT based denoising offers robustness under low SNR and nonstationary noise scenarios without additional computational overhead, highlighting its potential as a scalable pathway toward quantum-enhanced speech processing.

A Highly Robust Sparse Fractal Array

The term fractal refers to the fractional dimensions that have recursive nature and exhibit better array factor properties. In this article, we present a new class of sparse array where the recursive nature of a fractal can be used in designing an antenna array called a sparse fractal array by combining sparsity properties of the various sparse array and the recursive nature of fractal array. The most important property of the proposed array is the hole-free difference coarray which makes it a good choice for DOA estimation as various algorithms like coarray MUSIC etc demand hole-free difference coarray. But the performance of any array depends on the presence of essential and nonessential sensors in it which governs whether the difference coarray will get affected upon sensor failure or not. Hence, in this paper, a rigorous analysis is done for various combinations of sparse fractal arrays to test their robustness in presence of a faulty sensor environment.

A High Resolution Optimum 2D Coprime Planar Array

Designing a new class of rectangular two-dimensional sparse array to enhance the signal resolving capabilities with a limited number of sensors has always been a challenge. We explore the non-uniformity of the sparse arrays to enhance the Degrees of Freedom (DOF) by considering the under-determined cases using the concept of the virtual array. In this paper, we propose a two-dimensional novel sparse array configurations to estimate both azimuth and elevation angle of arrival of the signal. We propose a new class of rectangular two-dimensional arrays with sensors on a plane, whose difference co-array can give rise to a virtual two-dimensional planar array with a much larger number of elements, leading to an increase in the fully augmentable range of the array. The difference co-array of the proposed rectangular array gives a contiguous dense structure which leads to a reduction in holes within the planar array as compared to the traditional methods of CPA geometry in which there are many holes present in the outer dimension of the array structure as well as within the array as well. Moreover, we know that the angular resolution of an array is highly dependent on its beamwidth which itself is inversely proportional to array size or effective array aperture. Hence by increasing the array dimension a higher resolution can be achieved. The proposed arrays provide a higher DOF for the given number of physical sensors. Optimization is performed on the proposed geometry to maximize the directivity in the array steering look-up direction by suppressing the sidelobe levels to a greater extent.