Complexity-entropy causality plane: a useful approach for distinguishing songs

TL;DR

This paper introduces a complexity-entropy causality plane method for analyzing large music databases, effectively discriminating and comparing songs based on their complexity and entropy measures.

Contribution

It presents a novel, simple, and robust approach combining permutation entropy and complexity measures to distinguish and analyze songs in large datasets.

Findings

Effective discrimination of songs based on complexity-entropy measures

The method is simple, robust, and computationally fast

Potential for practical applications in music data analysis

Abstract

Nowadays we are often faced with huge databases resulting from the rapid growth of data storage technologies. This is particularly true when dealing with music databases. In this context, it is essential to have techniques and tools able to discriminate properties from these massive sets. In this work, we report on a statistical analysis of more than ten thousand songs aiming to obtain a complexity hierarchy. Our approach is based on the estimation of the permutation entropy combined with an intensive complexity measure, building up the complexity-entropy causality plane. The results obtained indicate that this representation space is very promising to discriminate songs as well as to allow a relative quantitative comparison among songs. Additionally, we believe that the here-reported method may be applied in practical situations since it is simple, robust and has a fast numerical implementation.