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arXiv · 2502.21097

Deep learning-based filtering of cross-spectral matrices using generative adversarial networks

Christof Puhle
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Abstract

In this paper, we present a deep-learning method to filter out effects such as ambient noise, reflections, or source directivity from microphone array data represented as cross-spectral matrices. Specifically, we focus on a generative adversarial network (GAN) architecture designed to transform fixed-size cross-spectral matrices. Theses models were trained using sound pressure simulations of varying complexity developed for this purpose. Based on the results from applying these methods in a hyperparameter optimization of an auto-encoding task, we trained the optimized model to perform five distinct transformation tasks derived from different complexities inherent in our sound pressure simulations.

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Christof Puhle. 2025-02-28. Deep learning-based filtering of cross-spectral matrices using generative adversarial networks. https://arxiv.org/abs/2502.21097

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