Computer Science Artificial Intelligence Music and Artificial Intelligence
Music and Artificial Intelligence
Music and Artificial Intelligence is an interdisciplinary field that applies machine learning and computational methods to analyze, generate, and interact with musical content. This encompasses a wide range of applications, from algorithmic composition that creates novel pieces in various styles and music information retrieval (MIR) for classifying genre and mood, to powering the personalized recommendation engines of streaming services and developing interactive systems capable of performing or improvising alongside human musicians. By learning complex patterns from vast datasets of audio and scores, AI is fundamentally changing how music is created, discovered, and experienced.
1.1.
Core Musical Concepts for Computation
1.1.1.
Pitch and Melody
1.1.1.1. Pitch Representation
1.1.1.1.1. Frequency in Hertz
1.1.1.1.2. MIDI Note Numbers
1.1.1.1.3. Scientific Pitch Notation
1.1.1.1.4. Cents and Microtonal Systems
1.1.1.2.1. Ascending and Descending Patterns
1.1.1.2.2. Stepwise Motion
1.1.1.2.4. Contour Analysis Algorithms
1.1.1.3. Interval Analysis
1.1.1.3.9. Interval Quality
1.1.1.3.10. Interval Detection Algorithms
1.1.1.4. Scale and Mode Recognition
1.1.1.4.2. Natural Minor Scales
1.1.1.4.3. Harmonic Minor Scales
1.1.1.4.4. Melodic Minor Scales
1.1.1.4.6. Pentatonic Scales
1.1.1.4.7. Chromatic Scales
1.1.1.4.8. Scale Detection Algorithms
1.1.2.
Rhythm and Timing
1.1.2.1.1. Beat Definition
1.1.2.1.2. Pulse Detection
1.1.2.1.3. Beat Tracking Algorithms
1.1.2.1.4. Metrical Hierarchy
1.1.2.2. Meter and Time Signatures
1.1.2.2.3. Irregular Meter
1.1.2.2.5. Time Signature Recognition
1.1.2.3.1. Beats Per Minute
1.1.2.3.2. Tempo Estimation Techniques
1.1.2.3.3. Tempo Variability
1.1.2.3.5. Accelerando and Ritardando
1.1.2.4. Rhythmic Patterns
1.1.2.4.5. Pattern Extraction
1.1.2.5.1. Grid-based Quantization
1.1.2.5.2. Swing Quantization
1.1.2.5.3. Groove Quantization
1.1.3.
Harmony and Tonality
1.1.3.1.1.3. Diminished Triads
1.1.3.1.1.4. Augmented Triads
1.1.3.1.2.1. Major Seventh
1.1.3.1.2.2. Minor Seventh
1.1.3.1.2.3. Dominant Seventh
1.1.3.1.2.4. Half-Diminished Seventh
1.1.3.1.2.5. Fully Diminished Seventh
1.1.3.1.3. Extended Chords
1.1.3.1.3.2. Eleventh Chords
1.1.3.1.3.3. Thirteenth Chords
1.1.3.1.6. Chord Inversions
1.1.3.2. Chord Progressions
1.1.3.2.1. Diatonic Progressions
1.1.3.2.2. Common Progressions
1.1.3.2.3. Circle of Fifths Progressions
1.1.3.2.4. Functional Harmony
1.1.3.2.4.1. Tonic Function
1.1.3.2.4.2. Subdominant Function
1.1.3.2.4.3. Dominant Function
1.1.3.2.5. Progression Detection Algorithms
1.1.3.3.6. Key Detection Methods
1.1.3.3.7.1. Common Chord Modulation
1.1.3.3.7.2. Chromatic Modulation
1.1.3.3.7.3. Enharmonic Modulation
1.1.3.3.9. Tonal Center Identification
1.1.3.4. Non-Tonal Systems
1.1.3.4.2. Twelve-Tone Technique
1.1.3.4.4. Pitch Class Sets
1.1.4.
Musical Form and Structure
1.1.4.1. Motivic Development
1.1.4.1.1. Motif Identification
1.1.4.1.2. Theme Recognition
1.1.4.1.3. Motivic Transformation
1.1.4.2.1. Antecedent and Consequent
1.1.4.2.2. Period Structure
1.1.4.2.3. Sentence Structure
1.1.4.2.4. Phrase Extension
1.1.4.3.4. Theme and Variations
1.1.4.4. Popular Music Forms
1.1.4.4.1. Verse-Chorus Form
1.1.4.4.3. Twelve-Bar Blues
1.1.4.4.4. Thirty-Two Bar Song Form
1.1.4.5. Repetition and Variation
1.1.4.5.1. Exact Repetition
1.1.4.5.2. Modified Repetition
1.1.4.5.3. Sequential Repetition
1.1.4.5.6. Thematic Development
1.1.4.6. Section Boundary Detection
1.1.4.6.1. Structural Analysis Algorithms
1.1.4.6.2. Form Recognition Systems
1.1.5.
Timbre and Instrumentation
1.1.5.1. Acoustic Properties of Sound
1.1.5.1.1. Fundamental Frequency
1.1.5.1.2. Harmonics and Overtones
1.1.5.1.4. Envelope Characteristics
1.1.5.1.5. Spectral Properties
1.1.5.1.5.1. Harmonic Content
1.1.5.1.5.2. Inharmonic Content
1.1.5.1.5.3. Spectral Centroid
1.1.5.1.5.4. Spectral Rolloff
1.1.5.1.5.5. Spectral Flatness
1.1.5.1.5.6. Spectral Brightness
1.1.5.2. Instrument Classification
1.1.5.2.1.1. Bowed Strings
1.1.5.2.1.2. Plucked Strings
1.1.5.2.1.3. Struck Strings
1.1.5.2.3.1. Lip Reed Instruments
1.1.5.2.4.1. Pitched Percussion
1.1.5.2.4.2. Unpitched Percussion
1.1.5.2.4.3. Membranophones
1.1.5.2.5. Electronic Instruments
1.1.5.2.5.3. Digital Instruments
1.1.5.3. Playing Techniques
1.1.5.3.1. String Techniques
1.1.5.3.2. Wind Techniques
1.1.5.3.2.3. Flutter Tonguing
1.1.5.3.3. Extended Techniques
1.1.5.3.4. Articulation Markings
1.1.5.4. Timbre Recognition
1.1.5.4.1. Instrument Identification
1.1.5.4.2. Timbre Signatures
1.1.5.4.3. Multi-instrument Recognition
1.2.
Digital Representation of Music
1.2.1.
Symbolic Representations
1.2.1.1.1.3. Control Change
1.2.1.1.1.4. Program Change
1.2.1.1.5. Note Velocities
1.2.1.1.6. Timing Resolution
1.2.1.2.1. Score Information
1.2.1.2.3. Measure Organization
1.2.1.2.4. Note Representation
1.2.1.2.5. Articulation Encoding
1.2.1.2.6. Dynamics Representation
1.2.1.2.8. Key and Time Signatures
1.2.1.3. Piano Roll Notation
1.2.1.3.1. Grid-based Representation
1.2.1.3.2. Note Duration Visualization
1.2.1.3.3. Velocity Representation
1.2.1.3.4. Editing Capabilities
1.2.1.4. Other Symbolic Formats
1.2.1.4.5. Humdrum Toolkit
1.2.2.
Audio Representations
1.2.2.1. Digital Audio Fundamentals
1.2.2.1.1. Analog-to-Digital Conversion
1.2.2.1.2. Sampling Theory
1.2.2.1.3. Nyquist Theorem
1.2.2.1.5. Quantization Error
1.2.2.2. Raw Waveform Representation
1.2.2.2.1. Pulse Code Modulation
1.2.2.2.4. Channel Configuration
1.2.2.2.4.3. Surround Sound
1.2.2.3. Time-Domain Analysis
1.2.2.3.1. Amplitude Envelope
1.2.2.3.2. Zero-Crossing Rate
1.2.2.3.3. Energy and Power
1.2.2.3.4. Autocorrelation
1.2.2.4. Frequency-Domain Analysis
1.2.2.4.1. Fourier Transform
1.2.2.4.2. Discrete Fourier Transform
1.2.2.4.3. Fast Fourier Transform
1.2.2.4.4. Short-Time Fourier Transform
1.2.2.4.5.1. Linear Spectrograms
1.2.2.4.5.2. Log-Frequency Spectrograms
1.2.2.4.5.3. Mel Spectrograms
1.2.2.4.6. Phase Information
1.2.2.5. Audio File Formats
1.2.2.5.1. Uncompressed Formats
1.2.2.5.2. Lossless Compression
1.2.2.5.3. Lossy Compression
1.2.2.5.4. Compression Algorithms
1.2.2.5.5. Perceptual Coding
1.3.
Fundamental AI and Machine Learning Concepts
1.3.1.
Learning Paradigms
1.3.1.1. Supervised Learning
1.3.1.1.1. Training Data Requirements
1.3.1.1.2. Labeled Examples
1.3.1.1.3. Feature-Target Relationships
1.3.1.1.4. Classification Tasks
1.3.1.1.5. Regression Tasks
1.3.1.1.6. Overfitting and Underfitting
1.3.1.1.7. Cross-Validation
1.3.1.2. Unsupervised Learning
1.3.1.2.1. Pattern Discovery
1.3.1.2.2. Clustering Algorithms
1.3.1.2.2.2. Hierarchical Clustering
1.3.1.2.3. Dimensionality Reduction
1.3.1.2.3.1. Principal Component Analysis
1.3.1.2.4. Association Rules
1.3.1.3. Semi-Supervised Learning
1.3.1.3.1. Limited Labeled Data
1.3.1.4. Reinforcement Learning
1.3.1.4.1. Agent-Environment Interaction
1.3.1.4.2. States and Actions
1.3.1.4.3. Reward Functions
1.3.1.4.4. Policy Learning
1.3.1.4.6. Policy Gradient Methods
1.3.2.
Classical Machine Learning Models
1.3.2.1.1. Linear Regression
1.3.2.1.2. Logistic Regression
1.3.2.1.3. Ridge Regression
1.3.2.1.4. Lasso Regression
1.3.2.2. Tree-Based Methods
1.3.2.2.3. Gradient Boosting
1.3.2.3. Instance-Based Learning
1.3.2.3.1. K-Nearest Neighbors
1.3.2.3.2. Distance Metrics
1.3.2.3.3. Curse of Dimensionality
1.3.2.4. Support Vector Machines
1.3.2.6. Probabilistic Models
1.3.2.6.2. Gaussian Mixture Models
1.3.2.6.3. Hidden Markov Models
1.3.3.
Deep Learning Fundamentals
1.3.3.1. Artificial Neural Networks
1.3.3.1.2. Multi-Layer Perceptron
1.3.3.1.3. Universal Approximation Theorem
1.3.3.1.4. Network Architecture Design
1.3.3.2. Training Neural Networks
1.3.3.2.1. Backpropagation Algorithm
1.3.3.2.2. Gradient Descent
1.3.3.2.2.1. Batch Gradient Descent
1.3.3.2.2.2. Stochastic Gradient Descent
1.3.3.2.2.3. Mini-Batch Gradient Descent
1.3.3.2.3. Optimization Algorithms
1.3.3.2.4.1. Mean Squared Error
1.3.3.2.4.2. Cross-Entropy Loss
1.3.3.3. Activation Functions
1.3.3.3.2. Hyperbolic Tangent
1.3.3.3.3. Rectified Linear Unit
1.3.3.4. Regularization Techniques
1.3.3.4.2. Batch Normalization
1.3.3.4.3. Layer Normalization
1.3.4.
Evaluation and Validation
1.3.4.1. Performance Metrics
1.3.4.1.1. Classification Metrics
1.3.4.1.2. Regression Metrics
1.3.4.1.2.1. Mean Absolute Error
1.3.4.1.2.2. Root Mean Square Error
1.3.4.2.1. Training-Validation-Test Split
1.3.4.2.2. K-Fold Cross-Validation
1.3.4.2.3. Hyperparameter Tuning
1.3.4.2.6. Bayesian Optimization
1.3.4.3. Bias-Variance Tradeoff
1.3.4.3.1. Model Complexity
1.3.4.3.2. Generalization Error
1.3.4.3.3. Learning Curves