Regularized inverse filtering and machine learning methods for speech enhancement - the Helsinki Speech Challenge 2024

Speech enhancement can be seen as an ill-posed inverse problem modeled by convolution with an impulse response, where the goal is to recover the clean speech signal from a corrupted one. In this work, we propose various methods for solving this problem in the cases of low-pass filtered and reverberated speech signals. Based on energy time curves, and energy decay curves we first estimate the impulse response functions, which can then be used as inverse filters in a deconvolution. We propose methods combining in different ways spectral subtraction, deconvolution by inverse filtering, regularized inverse filtering, and a machine learning method based on convolutional neural networks. We systematically collect results for the performance of the methods on different cases and different levels of complexity. The results highlight that no single method is superior across all tasks and levels, but in general a successful approach should be based on both spectral subtraction and a mathematical impulse response model, possibly together with a neural network. The work
was done in the context of the Helsinki Speech Challenge 2024.