# Mixed penalization in convolutive nonnegative matrix factorization for blind speech dereverberation

## TL;DR

This paper introduces a novel convolutive nonnegative matrix factorization approach with mixed penalization to improve blind speech dereverberation, demonstrating significant performance gains over existing methods.

## Contribution

It proposes a new method combining two penalizers in convolutive NMF for better speech dereverberation in general conditions.

## Key findings

- Significant improvement over state-of-the-art dereverberation methods.
- Effective in restoring speech signals affected by room reverberation.
- Algorithm demonstrates practical applicability in real-world scenarios.

## Abstract

When a signal is recorded in an enclosed room, it typically gets affected by reverberation. This degradation represents a problem when dealing with audio signals, particularly in the field of speech signal processing, such as automatic speech recognition. Although there are some approaches to deal with this issue that are quite satisfactory under certain conditions, constructing a method that works well in a general context still poses a significant challenge. In this article, we propose a method based on convolutive nonnegative matrix factorization that mixes two penalizers in order to impose certain characteristics over the time-frequency components of the restored signal and the reverberant components. An algorithm for implementing the method is described and tested. Comparisons of the results against those obtained with state of the art methods are presented, showing significant improvement.