Download Automatic Music Genre Classification PDF

TitleAutomatic Music Genre Classification
File Size1.5 MB
Total Pages175
Table of Contents
                            Dedication
Related Papers
Acknowledgements
Declaration
	Contents
	List of Figures
	List of Tables
Introduction, Review and Design
	1 Introduction
		1.1 Motivation
		1.2 The Research Problem
		1.3 Research Methodology Overview
	2 The Fundamentals of Musical Aspects
	3 Related Work
		3.1 Introduction
		3.2 Content-based Feature Extraction
			3.2.1 Timbre Content-based Features
			3.2.2 Rhythmic Content-based Features
			3.2.3 Pitch Content-based Features
		3.3 Related Classification Techniques
		3.4 Music Information Retrieval
		3.5 Contributions
	4 The Research Design
		4.1 Research Design
			4.1.1 Features
			4.1.2 Multi-class Classification
				4.1.2.1 The One-verses-all Paradigm
				4.1.2.2 The One-verses-one Paradigm
			4.1.3 GTZAN Dataset
			4.1.4 Feature Selection
				4.1.4.1 The Wrapper Method
				4.1.4.2 The Filter Method
Feature Analysis
	5 Feature Representation
		5.1 Introduction
		5.2 Test For Normality
			5.2.1 Discussion and Conclusion
		5.3 Other Feature Representations
		5.4 Conclusion and Discussion
	6 Magnitude Based Features
		6.1 Introduction
		6.2 The Magnitude Spectrum
			6.2.1 Spectral Slope
				6.2.1.1 Strongest Frequency
			6.2.2 Compactness
			6.2.3 Spectral Decrease
			6.2.4 Loudness
				6.2.4.1 Perceptual Sharpness
				6.2.4.2 Perceptual Spread
			6.2.5 Onset Detection
			6.2.6 Octave Band Signal Intensity
			6.2.7 Peak Detection
				6.2.7.1 Peak Centroid
				6.2.7.2 Peak Flux
				6.2.7.3 Spectral Crest Factor
				6.2.7.4 Peak Smoothness
			6.2.8 Spectral Flux
			6.2.9 Spectral Variability
		6.3 The Power Cepstrum
			6.3.1 Mel-Frequency Cepstral Coefficients
			6.3.2 Flatness
			6.3.3 Spectral Shape Statistics
				6.3.3.1 Spectral Centroid
				6.3.3.2 Spread, Kurtosis and Skewness
			6.3.4 Spectral Rolloff
		6.4 Conclusion and Discussion
	7 Tempo Detection
		7.1 Introduction
		7.2 Energy
			7.2.1 Beat Histogram
		7.3 Conclusion and Discussion
	8 Pitch and Speech Detection
		8.1 Introduction
		8.2 Pitch Detection
			8.2.1 Amplitude Modulation
			8.2.2 Zero Crossing Rate
		8.3 Speech Detection
			8.3.1 Autocorrelation Coefficients
		8.4 Envelope Shape Statistics
		8.5 Conclusion and Discussion
	9 Chordal Progressions
		9.1 Introduction
			9.1.1 Results
			9.1.2 Discussion
		9.2 MFCC-based Chroma vs. Mean-based Chroma
		9.3 Conclusion
Music Genre Classification
	10 Automatic Music Genre Classification
		10.1 Introduction
		10.2 Information Gain Ranking
		10.3 Automatic Genre Classification
		10.4 Conclusion
	11 Conclusion and Future Work
Appendix
	A Fundamental Mathematical Concepts
		A.1 Root Mean Square
		A.2 Arithmetic Mean
		A.3 Geometric Mean
		A.4 Euclidean Distance
		A.5 Weighted Mean
		A.6 Convolution
		A.7 Complex Conjugate
		A.8 Hanning Window
	B Additional Tables and Figures
	C Classification Algorithms
		C.0.1 Support Vector Machines
			C.0.2 Naïve Bayes Classifier
				C.0.2.1 Introduction
				C.0.2.2 The Naïve Bayes Classification Process
			C.0.3 K - Nearest Neighbours
	Bibliography
                        

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