1. TEMPLATE DESIGN © 2008 www.PosterPresentations.com Self Organized Neural Networks Applied to Animal Communication Abstract Background 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 Feature SelectionPerformance 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. Conclusion Acknowledgement s 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. 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. 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. Matthew Bradley 1 Kay Jantharasorn 2 Keith Jones 1 Advisor: Dr. Mohamed Zohdy 3 1 Oakland University 2 University of Michigan- Flint 3 Department of Electrical and Computer Engineering at Oakland University 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. With History Without History 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: Distance Selection There are several different ways to compute distance(Shown to the right): 1-Norm(Manhattan) 2-Norm(Euclidean) p-norm Infinity References Kohonen, T., Self-Organizating Maps, New York : Springer- Verlag, 1997 Payne, Roger S. McVay, Scott. “Songs of Humpback Whales.” Science 173 (1971): 585-597 Germano, Tom. 23 March, 1999. 2 June, 2008. 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. 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 time- frequency domain at 63.5%, and lastly the time- domain at 55.5% accuracy. W ij-- Neuron Weights α(t)- Learning Rate β(t)- Neighborhood Function X -- Input Vector 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. Frequency Domain Frequency Domain Time Domain Time/Frequency Filtered 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.