1. To Understand, Survey and Implement Neurodynamic Models By Farhan Tauheed Asif Tasleem

2. Project Progress ► Literature Review  Temporal Networks ► Specific Problem for Implementation  Implications ► Architectural Plan for Implementation  Formal definition

3. Motivation ► Machine Perception ► Biological aspects of Traditional Neural Network Models  Summation neuron  Non Linear Activation function ► Non biological aspects  Static  Continuous Input  Back propagation learning algorithm

4. Temporal Neural Networks ► Biologically Inspired  Continuous data feed is operated on  Dynamic Model  Long term Memory  Short term Memory ► Tapped delay line ► Distributed Time Lagged Feed forward NNs  Different Back Propagation algorithm

5. Literature Review ► Universal Myopic Mapping theorem  Any uniform fading memory mapped behind a static network can simulate just as well ► Fontine and Shastri 1993. have demostrated that certain tasks not having an explicit temporal aspect can also be processed advantageously by Temporal Networks ► Thompson(1996) “Completeness of BSS”

6. Related Problems ► Time Series Data Prediction ► Blind Signal Separation ► Cocktail Party Problem ► Attention Based Search Optimization ► Visual Pattern Recognition

7. Blind Signal Separation Implication ► Speech Recognition (phoneme recognition) ► Multimedia Compression ► MM database sound based retrieval ► Noise Removal ► Audio Analysis and Visualization ► Sonar and Radar ► Cache Hit Algorithms

8. Problem Decomposition ► Blind Signal Separation  General problem  No knowledge about the constituents ► Cocktail Party problem (Specific case)  Much restricted  Few sources  Can be many sensors  Source positioning can also be used as a cue

9. Continued ► Melody Decomposition (Specific Case)  Repetition in constituent signals (Cue)  Signals usually periodic  Difficulty (Scale invariant) ► Basic Keyword “DECONVULUTION”

10. Cocktail Party Problem ► Formal Problem Description  Given N signal sensors receiving N convolved signals made up of ‘d’ original signals such that d<N  We have to design an adaptive filter that masks each original signal from the rest ‘

11. Our Solution ► Assumptions  Ideal environment.. No noise.. No other signals other than ‘d’  We have prior knowledge of number of ‘d’  Number of sources is known (MIC) we’re experimenting with two ► Research being followed   COMBINING TIME-DELAYED DECORRELATION AND ICA:TOWARDS SOLVING THE COCKTAIL PARTY PROBLEM   By Te-Won Lee & Andreas Ziehe

12. Solution details ► Network Architecture  Single layer  Feed forward  Feedback (stability issues )  Sigmoid activation function  Learning rule (Maximizing joint entropy)  Frequency domain…FFT 1024 point  Tapped delay lines for short term memory ‘

13. Example

14. Current progress ► Things Done  Obtained binaural audio files  Implementation done in MATLAB ► Using Neural Network toolbox ► FFT function  Problem in training time due to FFT in training rule. ► Things TODO  Implementation complete / Optimize  Look into oscillatory networks