Mixed penalization in convolutive nonnegative matrix factorization for blind speech dereverberation
This addresses the problem of speech degradation in enclosed rooms for applications like automatic speech recognition, but it appears incremental as it builds on existing factorization approaches.
The authors tackled blind speech dereverberation by proposing a convolutive nonnegative matrix factorization method with mixed penalizers to restore signals and reduce reverberant components, showing significant improvement over state-of-the-art methods in comparisons.
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.