Music Genre Classification Alex Stabile. Example File

Slides:

Advertisements

Similar presentations

NEURAL NETWORKS Backpropagation Algorithm

Advertisements

Neural Networks  A neural network is a network of simulated neurons that can be used to recognize instances of patterns. NNs learn by searching through.

1 Part I Artificial Neural Networks Sofia Nikitaki.

Handwritten Character Recognition Using Artificial Neural Networks Shimie Atkins & Daniel Marco Supervisor: Johanan Erez Technion - Israel Institute of.

Neural Networks Basic concepts ArchitectureOperation.

The back-propagation training algorithm

1 Chapter 11 Neural Networks. 2 Chapter 11 Contents (1) l Biological Neurons l Artificial Neurons l Perceptrons l Multilayer Neural Networks l Backpropagation.

September 30, 2010Neural Networks Lecture 8: Backpropagation Learning 1 Sigmoidal Neurons In backpropagation networks, we typically choose  = 1 and 

S. Mandayam/ ANN/ECE Dept./Rowan University Artificial Neural Networks ECE /ECE Fall 2010 Shreekanth Mandayam ECE Department Rowan University.

S. Mandayam/ ANN/ECE Dept./Rowan University Artificial Neural Networks / Spring 2002 Shreekanth Mandayam Robi Polikar ECE Department.

Classification of Music According to Genres Using Neural Networks, Genetic Algorithms and Fuzzy Systems.

S. Mandayam/ ANN/ECE Dept./Rowan University Artificial Neural Networks ECE /ECE Fall 2006 Shreekanth Mandayam ECE Department Rowan University.

Traffic Sign Recognition Using Artificial Neural Network Radi Bekker

1 st Neural Network: AND function Threshold(Y) = 2 X1 Y X Y.

Artificial Neural Networks (ANN). Output Y is 1 if at least two of the three inputs are equal to 1.

Multiple-Layer Networks and Backpropagation Algorithms

Artificial Neural Networks

Kumar Srijan ( ) Syed Ahsan( ). Problem Statement To create a Neural Networks based multiclass object classifier which can do rotation,

Explorations in Neural Networks Tianhui Cai Period 3.

Chapter 9 Neural Network.

Waqas Haider Khan Bangyal. Multi-Layer Perceptron (MLP)

Appendix B: An Example of Back-propagation algorithm

Backpropagation An efficient way to compute the gradient Hung-yi Lee.

 Diagram of a Neuron  The Simple Perceptron  Multilayer Neural Network  What is Hidden Layer?  Why do we Need a Hidden Layer?  How do Multilayer.

Classification / Regression Neural Networks 2

1 Pattern Recognition: Statistical and Neural Lonnie C. Ludeman Lecture 21 Oct 28, 2005 Nanjing University of Science & Technology.

Artificial Neural Networks. The Brain How do brains work? How do human brains differ from that of other animals? Can we base models of artificial intelligence.

From Machine Learning to Deep Learning. Topics that I will Cover (subject to some minor adjustment) Week 2: Introduction to Deep Learning Week 3: Logistic.

1 Chapter 11 Neural Networks. 2 Chapter 11 Contents (1) l Biological Neurons l Artificial Neurons l Perceptrons l Multilayer Neural Networks l Backpropagation.

Introduction to the TLearn Simulator n CS/PY 399 Lab Presentation # 5 n February 8, 2001 n Mount Union College.

Multi-Layer Perceptron

Non-Bayes classifiers. Linear discriminants, neural networks.

 Based on observed functioning of human brain.  (Artificial Neural Networks (ANN)  Our view of neural networks is very simplistic.  We view a neural.

1 Lecture 6 Neural Network Training. 2 Neural Network Training Network training is basic to establishing the functional relationship between the inputs.

Procedure for Training a Child to Identify a Cat using 10,000 Example Cats For Cat_index  1 to Show cat and describe catlike features (Cat_index)

Chapter 8: Adaptive Networks

Hazırlayan NEURAL NETWORKS Backpropagation Network PROF. DR. YUSUF OYSAL.

Neural Networks Vladimir Pleskonjić 3188/ /20 Vladimir Pleskonjić General Feedforward neural networks Inputs are numeric features Outputs are in.

C - IT Acumens. COMIT Acumens. COM. To demonstrate the use of Neural Networks in the field of Character and Pattern Recognition by simulating a neural.

Artificial Intelligence CIS 342 The College of Saint Rose David Goldschmidt, Ph.D.

Alex Stabile. Research Questions: Could a computer learn to distinguish between different composers? Why does music by different composers even sound.

Bab 5 Classification: Alternative Techniques Part 4 Artificial Neural Networks Based Classifer.

Applications of Neural Networks Patrick Stalcup. Overview Background Vocabulary The Math Data Structure Design Black Boxing Example Applications.

Kim HS Introduction considering that the amount of MRI data to analyze in present-day clinical trials is often on the order of hundreds or.

Introduction to the TLearn Simulator n CS/PY 231 Lab Presentation # 5 n February 16, 2005 n Mount Union College.

Neural networks.

Neural Networks.

Deep Learning Amin Sobhani.

The Gradient Descent Algorithm

Spam Image Identification Using an Artificial Neural Network

Creating fuzzy rules from numerical data using a neural network

CSE 473 Introduction to Artificial Intelligence Neural Networks

Derivation of a Learning Rule for Perceptrons

Neural Networks A neural network is a network of simulated neurons that can be used to recognize instances of patterns. NNs learn by searching through.

CSE P573 Applications of Artificial Intelligence Neural Networks

Classification / Regression Neural Networks 2

Prof. Carolina Ruiz Department of Computer Science

Hebb and Perceptron.

Example: Voice Recognition

Artificial Neural Networks

CSE 573 Introduction to Artificial Intelligence Neural Networks

network of simple neuron-like computing elements

Backpropagation.

An Introduction To The Backpropagation Algorithm

Capabilities of Threshold Neurons

Computer Vision Lecture 19: Object Recognition III

Learning Combinational Logic

Artificial Neural Networks / Spring 2002

Principles of Back-Propagation

Prof. Carolina Ruiz Department of Computer Science

Presentation transcript:

Music Genre Classification Alex Stabile

Example File

Organization/Parsing file Beat class – Notes on beatNotes off beat – Beat number (8)

Chord Identification Notes: C, E, G What kind of chord? Look at intervals… E: m3, m6 -no matches G: P4, M6 -no matches C: M3, P5 -These intervals form a major chord, root position

Chord Identification Issue Non-chord tones: should be ignored in harmonic analysis Notes in first measure: C, E, G, D Considers each possible combination: – CEG – CDE – CDG

Analyzing Data—Machine Learning Approach Neural Networks: Each node has a value and an associated weight In the top layer, inputs become the nodes’ values Values are propagated through the network, creating values for the other nodes A simple neural network

Learning Algorithm The network is given a set of training data whose outputs are known Inputs are “fed” through the network: Calculated output is compared with desired output to obtain error

Learning Algorithm Back-propagation: the error is propagated backward though the network, and a respective error is calculated for each node The weights and node values are adjusted based on the errors so that a more desirable output will be obtained

Learning Algorithm For my project, the inputs to the network are the types and frequency of chords in a piece of music A threshold will be set for the output, based on the results of training: different ranges represent different genres