Artificial Neural Networks

1. Bruno Angeles McGill University – Schulich School of Music MUMT-621 Fall 2009 Artificial Neural Networks

2. Outline • Biological Neurons • To the Digital World • Activation Function • Feed-forward vs. Feedback • Applications • Training Methods • Conclusion

3. Biological Neurons • Synapses • Axon • Soma • Nucleus • Dendrites • ON/OFF • Threshold • 100 Hz [1]

4. Biological Neurons – Stimulation vs. Plasticity • A neuron excites another neuron repeatedly  ↑ strength of connection  easier for same excitation to occur • A neuron is not stimulated for a long time by another one  ↓ connection effectiveness (plasticity)

5. xn Wn … x3 W3 y W2 x2 W1 x1 To the Digital World • Inputs: x1..n • Weights: w1..n • Positive: excitory • Negative: inhibitory • Output • Summer: Σ • Activation function: • Step function • S-shaped • etc.

6. Activation Function Sigmoid function (s-shaped) Step function

7. Feed-forward Neural Network xn … yn x3 … x2 y1 x1 All arrows go in the direction of the outputs. This is the most popular way of connecting an Artificial Neural Network (ANN).

8. Feedback Neural Network xn … yn x3 … x2 y1 x1 Not all arrows go in the direction of the outputs.

9. Hidden layers Black Box xn … yn x3 … x2 y1 x1 Hidden Layers

10. ANN – Why, when? • when data is available, but not theory • when the input data is complex: no obvious pattern • when robustness to noise is wanted

11. Applications • Object Recognition • Medical Diagnosis • Obstacle Avoidance • Environment Exploration • Sales Forecasting • Marketing • Identifying Fraud http://www.youtube.com/watch?v=FKAULFV8tXw http://www.youtube.com/watch?v=nIRGz1GEzgI

12. Applications – Speech Technology and Music Technology • Recognition of human speakers • Text-to-speech applications • Transcription of polyphonic music • Music Information Retrieval http://www.youtube.com/watch?v=igNo-mPVYsw

13. Training the Network • Unsupervised Learning • Compression • Filtering • Supervised Learning • Pattern recognition • Function approximation • Reinforcement Learning • View on long-term success

14. Unsupervised Training • Cost function c(x,y) known • No known data set that minimizes c(x,y) • Try to minimize c(x,y)

15. Supervised Training – Backpropagation • Need ANN with hidden layer(s) • Need ANN with differentiable activation function • Randomly initialize all weights • Adjust each layer’s weights to minimize error

16. Supervised Training – Issues Overfitting Local minima [1] Solutions: • minimize # of neurons • jitter (add noise to input) • early stopping (training & validation) [1] Solution: momentum (including previous weight updates)

17. Reinforcement Learning • Long-term reward objective • At each frame, state of ANN is given • Choose among possible actions the one that maximizes final reward

18. Backpropagation – An Example 1 4 x1 2 6 y 5 x2 3 Each node n has weights and an activation function fn. z is the expected output given inputs x1 and x2. wij is the weight of node i’s input to node j. Propagate the error backwards  backpropagation.

19. Backpropagation – An Example 1 4 x1 2 6 y 5 x2 3 Now use the computed propagated errors to adjust the weights of all nodes. η controls the learning speed of the network. fnis the activation function of node n. u is fn’s response to the node’s inputs.

20. Conclusion • Pros: • Ability to learn  Flexible • Powerful • “Black box” • Cons: • Pitfalls • Example of tanks • Can be combined with other methods

21. Thank you!

22. Bibliography [1] Buckland, Mat. 2002. AI Techniques For Game Programming: Premier Press. [2] Karaali, O., G. Corrigan, N. Massey, C. Miller, O. Schnurr, and A. Mackie. 1998. A high quality text-to-speech system composed of multiple neural networks. Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing 2:1237-40. [3] Marmanis, H., and D. Babenko. 2009. Algorithms of the Intelligent Web: Manning Publications. [4] Marolt, M. 2001. Transcription of polyphonic piano music with neural networks. Proceedings of the 10th Mediterranean Electrotechnical Conference, 2000. MELECON 2000. 2:512-5. [5] Murray, J. C., H. R. Erwin, and S. Wermter. 2009. Robotic sound-source localisation architecture using cross-correlation and recurrent neural networks. Neural Networks 22 (2):173-89. [6] Rho, S., B. Han, E. Hwang, and M. Kim. 2007. MUSEMBLE: A Music Retrieval System Based on Learning Environment. Proceedings of the 2007 IEEE International Conference on Multimedia and Expo:1463-6.