Title: Switching divergences for spectral learning in blind speech dereverberation,
Abstract: 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.