Reliable Situation Recognition
Based On Noise Levels
Author(s)
U.-P. K¨appeler, A. Gerhardt, C. Schieberle, M. Wiselka,
K. H¨aussermann, O. Zweigle & P. Levi
Abstract
Situation recognition based on audio signals can be used to determine situations in
a meeting room while protecting the privacy by recording and analyzing only the
noise level instead of the complete audio signal.
A reliable situation recognition is normally obtained using Bayesian networks
which do not only rely on context information itself but additionally on corresponding
probabilities. Especially when the situation recognition itself should output
a quality rating to the determined situation it is necessary that each analyzed
information about all preconditions is rated with qualities or probabilities. This
leads to the need of a conversion from uncertainty to probabilities when using
sensor data to observe the environment to recognize situations.
In this paper we compare different methods for situation recognition based on
noise level measurements. We implemented a Multilayer Perceptron Neural Network,
a simple empiric method and we describe the advantages of our method
based on multinomial logistic regression which we adapted to a reliable and easily
configurable situation recognition based on sensor data. To evaluate the methods
we distinguished between the situations meeting, work and silence and recorded
thousands of noise levels to calibrate and compare the different methods against
each other.
Our approach using logistic regression can be used as situation recognition
based on sensor data. It is not necessary that the input information contains quality
ratings but the system has to be calibrated with sensor data that can be assigned
to all the situations that have to be distinguished. Since the result of this method
contains probabilities to all situations it can also be used to analyze sensor data
related to single preconditions of complex situation recognition algorithms based
on Bayesian networks and different types of context information.
Keywords: sensor, situation recognition, probability, logistic regression, quality,
context.
Keywords
sensor, situation recognition, probability, logistic regression, quality,
context
Related Book
Other papers in this volume
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