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

Slides:

Advertisements

Similar presentations

Slides from: Doug Gray, David Poole

Advertisements

Multilayer Perceptrons 1. Overview  Recap of neural network theory  The multi-layered perceptron  Back-propagation  Introduction to training  Uses.

Machine Learning: Connectionist McCulloch-Pitts Neuron Perceptrons Multilayer Networks Support Vector Machines Feedback Networks Hopfield Networks.

Neural NetworksNN 11 Neural Networks Teacher: Elena Marchiori R4.47 Assistant: Kees Jong S2.22

Simple Neural Nets For Pattern Classification

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

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

Image Compression Using Neural Networks Vishal Agrawal (Y6541) Nandan Dubey (Y6279)

Face Recognition Using Neural Networks Presented By: Hadis Mohseni Leila Taghavi Atefeh Mirsafian.

Traffic Sign Recognition Using Artificial Neural Network Radi Bekker

EE513 Audio Signals and Systems Statistical Pattern Classification Kevin D. Donohue Electrical and Computer Engineering University of Kentucky.

Presented by: Kamakhaya Argulewar Guided by: Prof. Shweta V. Jain

Audio classification Discriminating speech, music and environmental audio Rajas A. Sambhare ECE 539.

„Bandwidth Extension of Speech Signals“ 2nd Workshop on Wideband Speech Quality in Terminals and Networks: Assessment and Prediction 22nd and 23rd June.

KOHONEN SELF ORGANISING MAP SEMINAR BY M.V.MAHENDRAN., Reg no: III SEM, M.E., Control And Instrumentation Engg.

Presentation on Neural Networks.. Basics Of Neural Networks Neural networks refers to a connectionist model that simulates the biophysical information.

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

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

Using Neural Networks in Database Mining Tino Jimenez CS157B MW 9-10:15 February 19, 2009.

Outline What Neural Networks are and why they are desirable Historical background Applications Strengths neural networks and advantages Status N.N and.

NEURAL NETWORKS FOR DATA MINING

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.

COMPARISON OF IMAGE ANALYSIS FOR THAI HANDWRITTEN CHARACTER RECOGNITION Olarik Surinta, chatklaw Jareanpon Department of Management Information System.

Supervisor: Dr. Eddie Jones Co-supervisor: Dr Martin Glavin Electronic Engineering Department Final Year Project 2008/09 Development of a Speaker Recognition/Verification.

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

Hidden Markov Classifiers for Music Genres. Igor Karpov Rice University Comp 540 Term Project Fall 2002.

Handwritten Recognition with Neural Network Chatklaw Jareanpon, Olarik Surinta Mahasarakham University.

Multi-Layer Perceptron

Akram Bitar and Larry Manevitz Department of Computer Science

Music Genre Classification Alex Stabile. Example File

Other NN Models Reinforcement learning (RL)

Neural Network Basics Anns are analytical systems that address problems whose solutions have not been explicitly formulated Structure in which multiple.

Learning to Detect Faces A Large-Scale Application of Machine Learning (This material is not in the text: for further information see the paper by P.

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

Introduction to Neural Networks Introduction to Neural Networks Applied to OCR and Speech Recognition An actual neuron A crude model of a neuron Computational.

Neural Networks Presented by M. Abbasi Course lecturer: Dr.Tohidkhah.

Neural Networks 2nd Edition Simon Haykin

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.

Supervised Learning – Network is presented with the input and the desired output. – Uses a set of inputs for which the desired outputs results / classes.

Chapter 11 – Neural Nets © Galit Shmueli and Peter Bruce 2010 Data Mining for Business Intelligence Shmueli, Patel & Bruce.

Data Mining: Concepts and Techniques1 Prediction Prediction vs. classification Classification predicts categorical class label Prediction predicts continuous-valued.

A Document-Level Sentiment Analysis Approach Using Artificial Neural Network and Sentiment Lexicons Yan Zhu.

Speech Recognition through Neural Networks By Mohammad Usman Afzal Mohammad Waseem.

Automatic Classification of Audio Data by Carlos H. L. Costa, Jaime D. Valle, Ro L. Koerich IEEE International Conference on Systems, Man, and Cybernetics.

[1] National Institute of Science & Technology Technical Seminar Presentation 2004 Suresh Chandra Martha National Institute of Science & Technology Audio.

Big data classification using neural network

Multiple-Layer Networks and Backpropagation Algorithms

Neural Network Architecture Session 2

Deep Learning Amin Sobhani.

Learning in Neural Networks

Data Mining, Neural Network and Genetic Programming

Neural Network Implementations on Parallel Architectures

ARTIFICIAL NEURAL NETWORKS

Presentation on Artificial Neural Network Based Pathological Voice Classification Using MFCC Features Presenter: Subash Chandra Pakhrin 072MSI616 MSC in.

Advanced Wireless Networks

Brian Whitman Paris Smaragdis MIT Media Lab

Unsupervised Learning and Autoencoders

Musical Style Classification

Neural Networks Advantages Criticism

Artificial Neural Network & Backpropagation Algorithm

network of simple neuron-like computing elements

An Improved Neural Network Algorithm for Classifying the Transmission Line Faults Slavko Vasilic Dr Mladen Kezunovic Texas A&M University.

EE513 Audio Signals and Systems

Neural Networks Geoff Hulten.

Department of Electrical Engineering

The Network Approach: Mind as a Web

Outline Announcement Neural networks Perceptrons - continued

Presentation transcript:

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

Progress Presentation by: Asma Amir Nazia Zaman

Architecture of System Feature Extraction Pre processor Neural Network + Fuzzy Systems Wav file Feature vector Pre processed frame vector Classification vector Genetic Algorith m

Music Genres Identified by the System The classification system analyzes the contents of a.wav music file in order to sort it into specific categories: classical, jazz, country, rap, punk, and techno.

Feature Extraction In order to classify music samples, the characteristics in both the time and frequency domains must be examined: 1.bandwidth 2.beat(tempo) variability 3.high pass filtering 4.number of FFT coefficients above threshold 5.power spectral density 6.smoothness in frequency domain 7.total power

AUDIO feature extraction is a necessary step for the classification tasks. Size of the music file Handling by the Neural Network Feature Extraction Function

Feature Extraction (contd) For feature extraction we plan to map music into a semantic space consisting of a feature vector, which could include various aspects such as Frequency Content, Frequency coefficients and volume or any other musically meaningful category.

Feature Extraction (contd) Frequency content is found to be an important feature for classifying music. Three different frequency content calculations can be included in the feature vectors. The first feature being amplitude values of the DFT of the signal. The second calculation can be to take the natural logarithm amplitude values. The final calculation can be to take the inverse DFT of the logarithm of the amplitude. The volume of a musical piece is easily calculated as the variance of the samples.

Structure of Neural Network After research, a three- layer back propagation neural network has been found to be most suitable.

Structure of Neural Network (cont) The processing is done by multiple, weighted layers of nodes. Each node is connected to every node in the next layer, and at each interface between nodes are connecting fibers weighted by a sum.

Structure of Neural Network (cont) The nodes at each successive layer sum their inputs, weight the sum, and produce an output. The output of the final layer is the output of the system. In this manner, an output is predicted given an input.

Structure of Neural Network (cont) The training determines the weighting on the nodes. For example, we train the neural network by giving it the vectors of signal processing data (bandwidth, power spectral density, etc.) as well as the corresponding classification of music. E.g: Classical music is denoted as [ ], jazz is denoted as [ ], etc., as shifted delta functions.

Structure of Neural Network (cont) The neural network takes the input and feeds it through the system, evaluating the output. It then changes the weights in order to get a more accurate output. It continues to run the inputs through the network multiple times until the error between its output and the output you gave it is below a defined tolerance level.

The value along each dimension of the vector space is computed as an output from a pattern classifier which is trained to measure a particular feature. For this an M Neural Network can be trained to recognize membership in the M feature classes.