We discuss how vocal disorders can be post-corrected via a simple nonlinear
noise reduction scheme. This work is motivated by the need of a better
understanding of voice dysfunctions. This would entail a twofold advantage for
affected patients: Physicians can perform better surgical interventions and on
the other hand researchers can try to build up devices that can help to improve
voice quality, i.e. in a phone conversation, avoiding any surgigal treatment.
As a first step, a proper signal classification is performed, through the idea
of geometric signal separation in a feature space. Then through the analysis of
the different regions populated by the samples coming from healthy people and
from patients affected by T1A glottis cancer, one is able to understand which
kind of interventions are necessary in order to correct the illness, i.e. to
move the corresponding feature vector from the sick region to the healthy one.
We discuss such a filter and show its performance.Comment: Computer Methods and Programs in Biomedicine, accepted for
publicatio
