jason a. hockman
Overvie hat is a neural netork? basics and architecture learning applications in music
History 1940s: William McCulloch defines neuron 1960s: Perceptron 1970s: limitations presented (Minsky) 1980s: resurgence due to backpropagation
For Which Tasks Are NN Suited? classification clustering prediction pattern recognition function approximation
What is an Artificial Neuron? synapse biological origin soma dendrite (David Cofer, 2002) axion
What is an Artificial Neural? synapse biological origin soma dendrite (David Cofer, 2002) axion abstracted neuron
What is an Artificial Neuron? x 1 1 x 2 2! x i i net y = f (! i x i ) i
What is an Artificial Neuron? x 1 1 x 2 2! x i i net y = f (! i x i ) i activation function
What is an Artificial Neuron? x 1 1 threshold x 2 2! 0 /1 x p p perceptron applet
neuron neuron neuron neuron only one so is scaled and passed on
neuron neuron to s are scaled summed then neuron neuron
hidden feedforard netork
hidden feedforard netork
Learning ANN provided ith / pairs cost = mapping v.! 1 0$ Perceptron # 0 1 & " % ADALINE backpropagation correlation competitive learning single- ANN
e r r o r i np u t hidden backpropagation
x 1 s to hidden h 1 value is no x 1 * x1 o 1 o 2...... o N h1 h 1 hidden x1 x 1 x 2...... x N backpropagation
x 1 s to hidden h 1 value is no x 1 * x1 o 1 o 2...... o N h1 sum (x 1 * x 1,,x N * x N ) o 1 = sum (h 1,, h M *respective eights) x1 h 1...... h M hidden x 1 x 2...... x N backpropagation
if o 1 value < thresh, = 0 o 1 o 1 o 2...... o N h1 h 1 hidden x1 x 1 x 2...... x N backpropagation
if o 1 value < thresh, = 0 o 1 o 1 o 2...... o N remainder passed backards through netork e r r o r h1 h 1 hidden x1 x 1 x 2...... x N backpropagation
if o 1 value < thresh, = 0 o 1 o 1 o 2...... o N remainder passed backards through netork e r r o r h1 h 1 hidden x1 and eights are reassessed x 1 x 2...... x N backpropagation
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)!
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
to-stage system partial tracking model filterbank hair cell model B b note recognition model note netorks adaptive oscillators
References Bishop,C. 2006. Pattern Recognition and Machine Learning. Ne York: Springer Science and Business Media: 225:32. Chen, Z. 2001. Data Mining and Uncertain Reasoning: An Integrated Approach. Ne York: Wiley and Sons: 211:49. Marolt, M. 2004. A connectionist approach to automatic transcription of polyphonic piano music. IEEE Transactions on Multimedia 6, no. 3 (June): 439-49. Lacoste, A., and D. Eck, 2007. A supervised classification algorithm for note onset detection. EURASIP Applied Signal Processing:1-13, 2007. Thagard, P., 2005. Mind: Introduction to Cognitive Science, second edition. Massachusetts: The MIT Press. Mozer, M, 1994. Neural netork music composition by prediction: Exploring the benefits of psychoacoustic constraints and multiscale processing. Connection Science, vol. 6, no. 2-3: 247:80.