Self Organized Neural Networks
Applied to Animal Communication
Abstract
In this project, Self Organizing Feature Maps are trained to categorize animal communication sounds
into danger, hunger, and mating calls for Humpback Whales, Bottlenose Dolphins and Coyotes. Features are
extracted in time domain, frequency domain, and joint time-frequency domain from audio files of animal
communication. Unknown calls are then fed into the map to identify the sound. Several contributions have
been made to the maps to improve the quality, speed, and accuracy. Correlation has been implemented as
the activation function to improve the accuracy of identifying sounds. A history function to update previous
winning nodes has been implemented to decrease the time necessary to complete the training phase.
Sammon mapping has also been proposed to provide better initialization of vectors. In addition, several types
of distance metrics are tested to find best matching units including a blended Manhattan-Euclidean distance
which has been found useful.
Matthew Bradley1 Kay Jantharasorn2 Keith Jones1
Advisor: Dr. Mohamed Zohdy3
Oakland University 2 University of Michigan- Flint 3Department of
Electrical and Computer Engineering at Oakland University
1
Feature Selection
Performance
W e extracted data from frequency domain, time domain and joint time-frequency domain of each
sound from each animal. We filtered frequency domain to minimize noise. We selected several
features from time and frequency domains as vectors and joint time – frequency domain is selected
as a matrix.
Frequency Domain
Filtered
Frequency Domain
Time Domain
It was found that the addition of a history to the SOM greatly improved performance. With a history
the SOM was able to reach a stable state within 50 iterations and the map preserved the topological
differences of the input space. Without history, the SOM took much longer than 50 iterations(100+)
to reach stable state and the overall quality was lower. Displayed below are maps created to
display the quality and topology of the map by computing the average differences a neuron has with
its neighbors.
Time/Frequency
Background
A Self-Organizing Feature Map(SOFM) is a type of unsupervised neural network designed to
map high dimensional spaces onto low dimensional spaces that can be easily understood and
visualized. The map consists of input nodes to which feature vectors of high dimension are
presented. SOFMs are trained on exemplary patterns using two stages: competitive and weight
update stage.
With History
Without History
When testing the accuracy of the maps it was
found that the SOFM that used features from
the frequency domain performed better overall
at approximately 74.6%, followed by the timefrequency domain at 63.5%, and lastly the timedomain at 55.5% accuracy.
Distance Selection
Wij-- Neuron Weights α(t)- Learning Rate β(t)- Neighborhood Function X -- Input Vector
Objective
The main objective of this project is to extract features from several animal sounds that were
communicated under situations of mating, danger, and foraging using MATLAB. An application was
developed in Java to train several SOFMs using those data. This allows us to discern the animal
sounds and the situation under which they’re used. We have made the following contributions in
this project:
On Self-Organizing Feature Map
• Input Presentation Order
• Winning neurons history
• Taking account of context in which the sounds are made
• Proposing Correlation to do finer level clustering
• Sammon’s projection introduced to provide better initialization to the SOFM
On Feature Selection Ordering
• Proposing Matrix Features in the case of joint time-frequency domain
• Using normalization of input data for better efficiency
• Pre-processing the input to remove noise
On Animal Sounds
• Humpback Whale
• Bottle nose dolphin
• Coyote
TEMPLATE DESIGN © 2008
www.PosterPresentations.com
Conclusion
There are several different ways to
compute distance(Shown to the right):
1-Norm(Manhattan)
2-Norm(Euclidean)
p-norm
Infinity
In conclusion, self-organizing feature maps were successful in categorizing and identifying danger, hunger,
and mating calls of Coyotes, Humpback Whales, and Bottlenose dolphins. The history function greatly
improves the performance during the training phase. Correlation increases the success rate of identifying
unknown sounds by 15-20% Blended distance not only has a significant effect in improvement of
computational time in our self-organizing feature maps but it can also be used in a number of applications.
Metrics between Euclidean and Manhattan are
useful to obtain best matching units when the
data has a rotational bias. A blended
Euclidean-Manhattan distance metric
(lambda) is proposed to approximate the
traditional Lp metric (for p = 1 to 2) while
costing considerably less computational time:
We are currently writing three papers to be submitted for publication and conferences. The main paper will
generalize our findings and be submitted to IEEE, while the other papers will specialize in either SOFM
contributions or animal communication and be submitted to conferences.
Acknowledgement s
We’d like to thank National Science Foundation and Oakland University for giving us this great opportunity to
explore and participate in research and learn more about research careers. We’d also like to thank our advisor,
Dr. Mohamed Zohdy for his guidance throughout this research, Doug Hunter for his scientific journals, articles
related to Humpback whales, and Humpback whales data.
References
Kohonen, T., Self-Organizating Maps, New York : SpringerVerlag, 1997
Payne, Roger S. McVay, Scott. “Songs of Humpback Whales.”
Science 173 (1971): 585-597
Germano, Tom. 23 March, 1999. 2 June, 2008.
<http://davis.wpi.edu/~matt/courses/soms/>
Hunter, Doug. Professor Emeritus. Biology. Oakland University
Nsour, Ahmad. Zohdy, Mohamed. “Self Organized Learning
Applied to Global Positioning System (GPS) Data”.
Oakland University
FindSounds. 2008. Comparisonics. 2 June, 2008.
<http://www.findsounds.com/>