By
reducing the annoyance that impulsive non-speech
sounds cause, SoundSmoothing also minimizes wearer
fatigue and can increase wearing time. In fact,
research shows in these types of noisy situations,
most hearing instrument wearers prefer
SoundSmoothing.
![Preferences for SoundSmoothing](pic3.gif)
Users prefer SoundSmoothing activated in most
situations! For "party noise" and "speech signals"
the participants favored the two different settings
equally. This was the expected finding, as these
signals are not affected by SoundSmoothing.
Coupled with the other components from the
state-of-the-art speech and noise management system
(noise reduction, speech enhancement) from many
Siemens hearing instruments, SoundSmoothing helps
deliver unmatched sound-processing for the purest
sound possible from a hearing instrument.
DataLearning™
DataLearning™
technology is configured to learn the wearer's
listening volume preferences for each listening
program. It not only collects, DataLearning
automatically applies the gathered data to the
wearer's benefit.
DataLearning automatically makes loudness
adjustments without requiring extra effort on the
part of a Hearing Care Professional. By providing
objective usage data for fine tuning, DataLearning
also enables faster and more precise fitting during
follow-up visits. DataLearning technology gives
professionals information about the wearer's
listening environments, wearing time, and more.
How does DataLearning work in daily life?
![DataLearning](pic2.jpg)
DataLearning minimizes the need for volume control
adjustments. The hearing instrument is configured to
remember changes during daily use to the user's
volume setting, no matter whether such changes are
made directly or through Siemens remote control
devices. When the user switches the instrument off
(e.g., at bedtime) and then on again (e.g., the next
morning), the instrument will calculate the learned
volume setting in less than one ms.
In other words,
the learned volume setting is calculated by
averaging all past volume changes, weighing more
heavily recent changes. Over time, DataLearning
assures that overall gain always or nearly always
is, at the desired level.