# Notes on Music Information Retrieval¶

## Introduction¶

1. About This Site
2. Why MIR?
3. Python Basics
4. Getting Good at IPython (ipynb)
5. Using Audio in IPython (ipynb)
6. NumPy and SciPy Basics (ipynb)

## Chapter 1: Basic Signal Manipulation¶

1. Overview of a Basic MIR System (ipynb)
2. Signal Representations (ipynb)
3. Onset Detection (ipynb)
4. Beat Tracking (ipynb)
5. Tempo Estimation (ipynb)
6. Exercise: Understanding Audio Features through Sonification (ipynb)

## Chapter 2: Feature Extraction¶

1. Basic Feature Extraction (ipynb)
2. Zero Crossing Rate (ipynb)
3. Spectral Features (ipynb)
4. Mel-Frequency Cepstral Coefficients (ipynb)

## Chapter 3: Classification¶

1. Basic Supervised Classification (ipynb)
2. K-Nearest Neighbor Classification (ipynb)
3. Exercise: K-Nearest Neighbor Instrument Classification (ipynb)
4. K-Means Clustering (ipynb)
5. Exercise: Unsupervised Instrument Classification using K-Means (ipynb)

## Chapter 4: Matrix Factorization and Evaluation¶

1. Nonnegative Matrix Factorization (ipynb)
2. Exercise: Source Separation using NMF (ipynb)
3. Classification of Separated Signals
4. Cross Validation
5. Evaluation

## Chapter 5: Music Fingerprinting¶

1. Locality Sensitive Hashing (ipynb)

## Appendix¶

1. Segmentation (ipynb)
2. Beat Tracking in Essentia (ipynb)
3. Spectral Features in Essentia (ipynb)
4. Feature Extraction in Essentia (ipynb)

## More (work in progress)¶

1. Tonal Descriptors: Pitch and Chroma