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Table of contents
Chapter 1 OVERVIEW
1.1 THE AMIR PROCESS
1.2 THESIS CHAPTERS
Chapter 2 INTRODUCTION
2.1 MOTIVATION
2.1.1 Intelligent search of music
2.1.2 Structured-Audio Encoding
2.1.3 Music Information Retrieval (MIR)
2.1.4 A tool for Composers and sound editors
2.2 CHALLENGES
2.2.1 Accuracy
2.2.2 Generality
2.2.3 Taxonomy
2.2.4 Data Validity
2.2.5 Polyphonicity
2.2.6 Pattern Recognition issues
Chapter 3 HISTORY
3.1 ISOLATED TONES
3.2 SOLO PERFORMANCES
3.3 MULTI-INSTRUMENTAL MUSIC
Chapter 4 TAXONOMIES
Chapter 5 DATA SETS
5.1 SEPARATE TONES
5.2 SOLO PERFORMANCES
5.3 AUTHENTIC DUOS – REAL PERFORMANCES
5.4 MULTI-INSTRUMENTAL SOLO MIXES
Chapter 6 FEATURE DESCRIPTORS
6.1 FEATURE TYPES
6.1.1 Temporal Features
6.1.2 Energy Features
6.1.3 Spectral Features
6.1.4 Harmonic Features
6.1.5 Perceptual Features
6.2 FEATURE LIST
Chapter 7 FEATURE WEIGHTING AND SELECTION
7.1 LINEAR DISCRIMINANT ANALYSIS
7.2 GRADUAL DESCRIPTOR ELIMINATION (GDE) USING DISCRIMINANT ANALYSIS
7.2.1 The GDE Algorithm
7.2.2 Example Evaluation
7.3 CORRELATION-BASED FEATURE SELECTION (CFS)
Chapter 8 CLASSIFICATION ALGORITHMS
8.1 NEURAL NETWORKS
8.1.1 Backpropagation (BP)
8.2 K-NEAREST NEIGHBORS (KNN)
8.2.1 Selection of “K”
8.3 CHOSEN CLASSIFICATION METHOD – « LDA+KNN »
Chapter 9 DIFFERENT EVALUATION TECHNIQUES AND THE IMPORTANCE OF CROSS DATABASE EVALUATION
9.1 INTRODUCTION
9.2 THE TESTING SET
9.2.1 The Sounds
9.2.2 Feature Descriptors
9.3 CLASSIFICATION ALGORITHMS
9.3.1 « LDA+KNN »
9.3.2 « BP80 »
9.4 EVALUATION METHODS
9.4.1 Self-Classification evaluation method
9.4.2 Mutual-Classification evaluation method
9.4.3 Minus-1-DB evaluation method
9.5 DISADVANTAGES OF SELF-CLASSIFICATION
9.6 CONCLUSIONS
9.7 MORE EVALUATION ALGORITHMS
9.7.1 Minus-1 Instrument Instance evaluation method
9.7.2 Minus-1-Solo Evaluation method
9.7.3 Leave-One-Out Cross validation Method
Chapter 10 IMPROVING THE CONSISTENCY OF SOUND DATABASES
10.1 ALGORITHMS FOR REMOVING OUTLIERS
10.1.1 Interquantile Range (IQR)
10.1.2 Modified IQR (MIQR)
10.1.3 Self-Classification Outlier removal (SCO)
10.2 CONTAMINATED DATABASE
10.3 EXPERIMENT
10.4 RESULTS
10.5 CONCLUSIONS
Chapter 11 AMIR OF SEPARATE TONES AND THE SIGNIFICANCE OF NON-HARMONIC “NOISE” VS. THE HARMONIC SERIES
11.1 INTRODUCTION
11.2 ORIGINAL SOUND SET
11.3 NOISE REMOVAL
11.4 HARMONIC SOUNDS AND RESIDUALS
11.5 FEATURE DESCRIPTORS
11.6 FEATURE SELECTION
11.7 CLASSIFICATION AND EVALUATION
11.8 RESULTS
11.8.1 Instrument Recognition
11.8.2 Best 10 Feature Descriptors
11.9 CONCLUSIONS
11.10 FUTURE WORK
Chapter 12 AMIR IN SOLOS
12.1 MOTIVATION
12.2 DATA SET
12.3 CLASSIFICATION
12.4 REALTIME SOLO RECOGNITION
12.5 MINUS-1-SOLO RESULTS
12.5.1 Realtime Feature Set
Chapter 13 AMIR IN MULTI-INSTRUMENTAL, POLYPHONIC MUSIC
13.1 AMIR METHODS FOR MIP MUSIC
13.1.1 “naïve” Solo Classifier
13.1.2 Source-Reduction (SR)
13.1.3 Harmonic-Resynthesis (HR)
13.2 EVALUATION RESULTS
13.2.1 Authentic Duo Recordings
13.3 SOLO MIXTURES
13.3.1 Independent Evaluation
13.3.2 Grading
13.3.3 Results
13.4 CONCLUSIONS
Chapter 14 SUMMARY
Chapter 15 FUTURE WORK
15.1 USING COMPOSITION RULES
15.2 FEATURE DESCRIPTORS
15.2.1 Utilizing information in the non-harmonic Residuals
15.2.2 Heuristic descriptors
15.2.3 Modelling Signal Evolution
15.3 PRACTICAL APPLICATIONS
15.3.1 Increasing the number of Instruments
15.3.2 Speed Improvement
15.4 PRECISE EVALUATION
15.5 HUMAN INTEGRATION
