McGill University > Schulich School of Music > MUMT 611 > Presentation III. Neural Networks. artificial. jason a. hockman
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1 jason a. hockman
2 Overvie hat is a neural netork? basics and architecture learning applications in music
3 History 1940s: William McCulloch defines neuron 1960s: Perceptron 1970s: limitations presented (Minsky) 1980s: resurgence due to backpropagation
4 For Which Tasks Are NN Suited? classification clustering prediction pattern recognition function approximation
5 What is an Artificial Neuron? synapse biological origin soma dendrite (David Cofer, 2002) axion
6 What is an Artificial Neural? synapse biological origin soma dendrite (David Cofer, 2002) axion abstracted neuron
7 What is an Artificial Neuron? x 1 1 x 2 2! x i i net y = f (! i x i ) i
8 What is an Artificial Neuron? x 1 1 x 2 2! x i i net y = f (! i x i ) i activation function
9 What is an Artificial Neuron? x 1 1 threshold x 2 2! 0 /1 x p p perceptron applet
10 neuron neuron neuron neuron only one so is scaled and passed on
11 neuron neuron to s are scaled summed then neuron neuron
12 hidden feedforard netork
13 hidden feedforard netork
14 Learning ANN provided ith / pairs cost = mapping v.! 1 0$ Perceptron # 0 1 & " % ADALINE backpropagation correlation competitive learning single- ANN
15 e r r o r i np u t hidden backpropagation
16 x 1 s to hidden h 1 value is no x 1 * x1 o 1 o o N h1 h 1 hidden x1 x 1 x x N backpropagation
17 x 1 s to hidden h 1 value is no x 1 * x1 o 1 o o N h1 sum (x 1 * x 1,,x N * x N ) o 1 = sum (h 1,, h M *respective eights) x1 h h M hidden x 1 x x N backpropagation
18 if o 1 value < thresh, = 0 o 1 o 1 o o N h1 h 1 hidden x1 x 1 x x N backpropagation
19 if o 1 value < thresh, = 0 o 1 o 1 o o N remainder passed backards through netork e r r o r h1 h 1 hidden x1 x 1 x x N backpropagation
20 if o 1 value < thresh, = 0 o 1 o 1 o o N remainder passed backards through netork e r r o r h1 h 1 hidden x1 and eights are reassessed x 1 x x N backpropagation
21 Applications in Music instrument classification (Kostek et al., 1997) genre classification (Bergstra et al., 2003) onset detection (Lacoste and Eck, 2007) beat, tempo and rhythm (1995+) music composition (Mozer, 1994)!
22 Applications in Audio A Connectionist Approach to Automatic Transcription of Polyphonic Piano Music Matija Marolt IEEE Transactions on Multimedia, vol.6, no.3, June 2004
23 to-stage system partial tracking model filterbank hair cell model B b note recognition model note netorks adaptive oscillators
24 References Bishop,C Pattern Recognition and Machine Learning. Ne York: Springer Science and Business Media: 225:32. Chen, Z Data Mining and Uncertain Reasoning: An Integrated Approach. Ne York: Wiley and Sons: 211:49. Marolt, M A connectionist approach to automatic transcription of polyphonic piano music. IEEE Transactions on Multimedia 6, no. 3 (June): Lacoste, A., and D. Eck, A supervised classification algorithm for note onset detection. EURASIP Applied Signal Processing:1-13, Thagard, P., Mind: Introduction to Cognitive Science, second edition. Massachusetts: The MIT Press. Mozer, M, Neural netork music composition by prediction: Exploring the benefits of psychoacoustic constraints and multiscale processing. Connection Science, vol. 6, no. 2-3: 247:80.
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