Music Genre Classification Project

This notebook will use various Python libraries to perform build a machine learning model using audio file metadata.

1. Problem definition

Given features extracted from an audio file, can we predict the genre that the audio belongs to

2. Data

The data came from the Free Music Archive developed by several people at École Polytechnique Fédérale de Lausann (EPFL) and Nanyang Technological University (NTU). The research paper can be found here and the GitHub repository containing the project files can be found here.

3. Evaluation

If we can reach accuracy that is above the highest benchmark recorded in the research paper, the project is complete.

Here is the benchmarks taken from the FMA research paper:

Feature set	LR	kNN	SVM	MLP
1 Chroma	44	44	48	49
2 Tonnetz	40	37	42	41
3 MFCC	58	55	61	53
4 Spec. centroid	42	45	46	48
5 Spec. bandwidth	41	45	44	45
6 Spec. contrast	51	50	54	53
7 Spec. rolloff	42	46	48	48
8 RMS energy	37	39	39	39
9 Zero-crossing rate	42	45	45	46
3 + 6	60	55	63	54
3 + 6 + 4	60	55	63	53
1 to 9	61	52	63	58

The values in the table represent the accuracy % of a feature set and a given model.

The models are defined as:

• LR = Linear Regression with an L² penalty
• kNN = k-nearest neighbours with k = 200
• SVM = support vector machines (SVM) with a radial basis function (RBF)
• MLP = multilayer perceptron (MLP) with 100 hidden neurons

4. Features

Here are the features that come with each audio file:

• Chroma
• Tonnetz
• Mel-frequency cepstral coefficients (MFCC)
• Spectral centroid
• Spectral bandwidth
• Spectral contrast
• Spectral rolloff
• RMS energy
• Zero crossing-rate