# A Music Classification Model based on Metric Learning and Feature Extraction from MP3 Audio Files

## TL;DR

This paper introduces a novel music classification model that uses metric learning and feature extraction from MP3 files, aiming to improve music similarity learning and enable personalized metrics for users.

## Contribution

It proposes a new metric learning approach with structured prediction for music genre classification, incorporating MFCC features and PCA for dimensionality reduction.

## Key findings

- Model shows promising accuracy compared to baseline algorithms
- Experiments validate the effectiveness of the metric learning approach
- Encourages development of an online learning version

## Abstract

The development of models for learning music similarity and feature extraction from audio media files is an increasingly important task for the entertainment industry. This work proposes a novel music classification model based on metric learning and feature extraction from MP3 audio files. The metric learning process considers the learning of a set of parameterized distances employing a structured prediction approach from a set of MP3 audio files containing several music genres. The main objective of this work is to make possible learning a personalized metric for each customer. To extract the acoustic information we use the Mel-Frequency Cepstral Coefficient (MFCC) and make a dimensionality reduction with the use of Principal Components Analysis. We attest the model validity performing a set of experiments and comparing the training and testing results with baseline algorithms, such as K-means and Soft Margin Linear Support Vector Machine (SVM). Experiments show promising results and encourage the future development of an online version of the learning model.

---
Source: https://tomesphere.com/paper/1905.12804