2 research outputs found
Mixed penalization in convolutive nonnegative matrix factorization for blind speech dereverberation
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
Switching divergences for spectral learning in blind speech dereverberation
When recorded in an enclosed room, a sound signal will most certainly get
affected by reverberation. This not only undermines audio quality, but also
poses a problem for many human-machine interaction technologies that use speech
as their input. In this work, a new blind, two-stage dereverberation approach
based in a generalized \beta-divergence as a fidelity term over a non-negative
representation is proposed. The first stage consists of learning the spectral
structure of the signal solely from the observed spectrogram, while the second
stage is devoted to model reverberation. Both steps are taken by minimizing a
cost function in which the aim is put either in constructing a dictionary or a
good representation by changing the divergence involved. In addition, an
approach for finding an optimal fidelity parameter for dictionary learning is
proposed. An algorithm for implementing the proposed method is described and
tested against state-of-the-art methods. Results show improvements for both
artificial reverberation and real recordings