Audio classification using ML methods

TL;DR

This paper demonstrates how various machine learning algorithms can be applied to classify music genres from audio features, focusing on distinguishing classical and metal music.

Contribution

It provides a practical implementation of feature extraction and classification techniques for music genre recognition using multiple supervised learning algorithms.

Findings

Different classifiers achieve varying accuracy levels.

Feature extraction is crucial for effective audio classification.

The code implementation facilitates reproducibility and further research.

Abstract

Machine Learning systems have achieved outstanding performance in different domains. In this paper machine learning methods have been applied to classification task to classify music genre. The code shows how to extract features from audio files and classify them using supervised learning into 2 genres namely classical and metal. Algorithms used are LogisticRegression, SVC using different kernals (linear, sigmoid, rbf and poly), KNeighborsClassifier , RandomForestClassifier, DecisionTreeClassifier and GaussianNB.