README.md

MusiCNN-embeddings

The project consists on evaluating music similarity and building a genre classifier using song embeddings from GTZAN dataset extracted with Essentia’s MSD-MusiCNN model.

The repository is divided in three different notebooks or modules:

1. Dataset generation

In this notebook, we will do all the necessary tasks to build a songs-embedding dataset from the GTZAN dataset in order to use it in several tasks: evaluating songs similarity and genre classification.

The steps are the following:

• Download GTZAN dataset with the help of mirdata library.
• Download Essentia’s MSD-MusiCNN TensorFlow model for the specific task of genre classification.
• Embeddings extraction: Extract songs-temporal embeddings using MusiCNN model and process them to get mean-embeddings.
• Labels processing: Encode genre labels to numeric values.
• Save data to npy files to use them in the following tasks.

2. Music similarity

In this notebook, we will evaluate music similarity based on previously extracted songs embeddings. Each song embedding in the dataset has a single ground-truth genre label associated. We will assume these genres as a proxy for evaluating similarity, that is, considering two songs are similar when they have the same genre label. Evaluation metrics such as average precision@10, MAP@10, and MAP@50 will be computed across the entire dataset

3. Genre classification

In this notebook, we will train and evaluate a new transfer learning classifier using the embeddings from GTZAN dataset, extracted by Essentia’s MSD-MusiCNN TensorFlow. We will use the audio features (embeddings) as an input to train a shallow target classifier inspired by the ones used in Alonso-Jiménez, P., Bogdanov, D., & Serra, X. (2020). Deep embeddings with Essentia models: a multilayer perceptron with a single hidden layer with 100 neurons and ReLU activations. The output layer uses softmax for predictions. To do this task, we will use Pytorch.

Usage

No package installation is required. You can run all the notebooks directly in Google Colab with the link located on top of each notebook.

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Author: Alba Mínguez Sánchez

March 2021