LNAI 2903 MML Classification of Music Genres 1st Edition by Adrian Bickerstaffe, Enes Makalic ISBN 9783540206460 354020646X

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ISBN 10: 354020646X
ISBN 13: 9783540206460
Author: Adrian Bickerstaffe, Enes Makalic

Inference of musical genre, whilst seemingly innate to the human mind, remains a challenging task for the machine learning community. Online music retrieval and automatic music generation are just two of many interesting applications that could benefit from such research. This paper applies four different classification methods to the task of distinguishing between rock and classical music styles. Each method uses the Minimum Message Length (MML) principle of statistical inference. The first, an unsupervised learning tool called Snob, performed very poorly. Three supervised classification methods, namely decision trees, decision graphs and neural networks, performed significantly better. The defining attributes of the two musical genres were found to be pitch mean and standard deviation, duration mean and standard deviation, along with counts of distinct pitches and rhythms per piece. Future work includes testing more attributes for significance, extending the classification to include more genres (for example, jazz, blues etcetera) and using probabilistic (rather than absolute) genre class assignment. Our research shows that the distribution of note pitch and duration can indeed distinguish between significantly different types of music.

LNAI 2903 MML Classification of Music Genres 1st Edition Table of contents:

Chapter 1: Introduction to Music Genre Classification

• 1.1. The Challenge of Music Genre Classification
• 1.2. Importance and Applications of Music Genre Classification
• 1.3. Overview of Machine Learning in Music Classification
• 1.4. Structure of the Book

Chapter 2: Fundamentals of Music Signal Processing

• 2.1. Music Data Representation
• 2.2. Audio Features and Their Role in Classification
• 2.3. Time-Domain, Frequency-Domain, and Spectral Analysis
• 2.4. Mel-frequency Cepstral Coefficients (MFCC)
• 2.5. Other Relevant Audio Features: Chroma, Rhythm, Tonal Features

Chapter 3: Introduction to Machine Learning for Music Classification

• 3.1. Overview of Machine Learning Techniques
• 3.2. Supervised vs. Unsupervised Learning for Music Classification
• 3.3. Feature Selection and Dimensionality Reduction
• 3.4. Evaluation Metrics in Music Genre Classification (Accuracy, Precision, Recall, F1-Score)

Chapter 4: Classical Machine Learning Algorithms

• 4.1. k-Nearest Neighbors (k-NN) in Music Genre Classification
• 4.2. Support Vector Machines (SVM) for Music Classification
• 4.3. Decision Trees and Random Forests
• 4.4. Naive Bayes Classifier
• 4.5. Linear and Logistic Regression Approaches

Chapter 5: Deep Learning Approaches to Music Genre Classification

• 5.1. Introduction to Deep Learning in Music Classification
• 5.2. Convolutional Neural Networks (CNNs) for Audio Data
• 5.3. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks
• 5.4. Hybrid Models: Combining CNNs and RNNs
• 5.5. Transfer Learning for Music Genre Classification

Chapter 6: Feature Engineering and Extraction for Music Genres

• 6.1. Raw Audio vs. Extracted Features
• 6.2. Time-Frequency Representations: Spectrograms and Wavelet Transforms
• 6.3. Advanced Feature Extraction Methods: Timbre, Harmony, and Rhythm
• 6.4. Feature Normalization and Standardization Techniques
• 6.5. Case Studies of Feature Engineering in Music Genre Classification

Chapter 7: Dataset Considerations and Preprocessing

• 7.1. Popular Datasets for Music Genre Classification (e.g., GTZAN, FMA, MagnaTagATune)
• 7.2. Data Augmentation and Preprocessing Techniques
• 7.3. Handling Missing Data in Music Datasets
• 7.4. Labeling and Annotation Challenges
• 7.5. Ethics and Privacy in Music Data Collection

Chapter 8: Model Evaluation and Performance Metrics

• 8.1. Cross-Validation and Train-Test Splitting Strategies
• 8.2. Confusion Matrix and Performance Measures
• 8.3. ROC Curve, Precision-Recall Curve, and Area Under Curve (AUC)
• 8.4. Hyperparameter Tuning and Model Optimization
• 8.5. Real-World Application: Music Genre Classification in Streaming Platforms

Chapter 9: Advanced Topics in Music Genre Classification

• 9.1. Multi-Label Classification: Music with Multiple Genres
• 9.2. Temporal Dynamics: Modeling Evolution of Music Genres Over Time
• 9.3. Transfer Learning and Domain Adaptation in Music Classification
• 9.4. Music Genre Classification in the Context of Emotion and Sentiment Analysis
• 9.5. Music and Genre Classification in Multimodal Systems (Combining Audio with Lyrics, Metadata, etc.)

Chapter 10: Case Studies and Practical Applications

• 10.1. Case Study 1: Classification of Popular Music Genres
• 10.2. Case Study 2: Genre Classification in Classical Music
• 10.3. Case Study 3: Real-Time Genre Classification for Music Recommendation Systems
• 10.4. Case Study 4: Music Classification in Digital Audio Workstations (DAWs)
• 10.5. Challenges and Lessons Learned from Industry Deployments

Chapter 11: Future Trends in Music Genre Classification

• 11.1. The Role of Artificial Intelligence in Music Discovery
• 11.2. Emerging Techniques: Generative Models and Music Synthesis
• 11.3. Music Genre Classification in the Age of AI-powered Music Recommendation Systems
• 11.4. Towards Universal Music Classification Systems
• 11.5. Closing Thoughts on the Future of Music and Technology

References

• Extensive list of references, including books, articles, and key papers related to music genre classification, machine learning, and audio processing.

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