
Transient
Noise
Reduction
9
effects
of
transients
and
possible
improvements
or
negative
side
effects
of
TNR.
Smart
algorithms
based
on
sound
environment
classification
would
be
a
desirable
development.
Another
limitation
of
this
study
is
that
we
included
good
performers
only
(CVC
scores
>70%).
The
effect
of
the
CNR
and
TNR
algorithms
is
not
necessarily
the
same
for
CI
users
with
less
benefit
of
the
CI.
These
CI
users
complain
more
often
that
sounds
are
too
loud
or
too
disturbing,
so
there
is
more
room
for
improvement,
at
least
for
listening
comfort.
On
the
other
hand,
the
effect
of
TNR
may
be
too
small
to
really
cause
a
significant
shift
in
listening
comfort
and
performance
as
noisy
conditions
remain
extremely
challenging
for
this
group
of
CI
users
We
conclude
that
the
investigated
TNR
algorithm
in
a
CI
processor
was
effective
in
reducing
annoyance
from
transient
sounds
with
high
peak
levels,
without
causing
a
negative
effect
on
speech
understanding.
However,
TNR
was
not
able
to
compensate
for
the
large
decrease
in
speech
understanding
caused
by
transient
sounds.
TNR
did
not
reduce
the
beneficial
effect
of
CNR
on
speech
intelligibility
in
noise,
but
no
cumulated
improvement
was
found
either.
Both
types
of
noise
reduction serve
different
goals
and
work
independently,
so
they
can
be
easily
combined
in
one
CI
system.
Acknowledgements
The
authors
gratefully
acknowledge
the
participants
and
they
thank
Phillipp
Hehrmann
for
analysis
of
the
signals
with
respect
of
TNR
activation
and
Martina
Brendel
for
her
helpful
comments.
Portions
of
the
data
were
presented
at
the
14th
International
Conference
on
Cochlear
Implants
(CI2016),
May
11-14,
2016,
Toronto.
Declaration
of
interest:
This
work
was
supported
by
Advanced
Bionics.
ORCID
J.
Gertjan
Dingemanse
e
http://orcid.org/0000-0001-8837-3474
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