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High-Level Libraries for Emotion Recognition in Music: A Review

  • Yesid Ospitia Medina
  • Sandra Baldassarri
  • José Ramón Beltrán
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 847)

Abstract

This article presents a review of high-level libraries that enable to recognize emotions in digital files of music. The main objective of the work is to study and compare different high-level content-analyzer libraries, showing their main functionalities, focused on the extraction of low and high level relevant features to classify musical pieces through an affective classification model. In addition, there has been a review of different works in which those libraries have been used to emotionally classify the musical pieces, through rhythmic and tonal features reconstruction, and the automatic annotation strategies applied, which generally incorporate machine learning techniques. For the comparative evaluation of the different high-level libraries, in addition to the common attributes in the chosen libraries, the most representative attributes in music emotion recognition field (MER) were selected. The comparative evaluation enables to identify the current development in MER regarding high-level libraries and to analyze the musical parameters that are related with emotions.

Keywords

MER (Music Emotion Recognition) MIR (Music Information Retrieval) API (Application Programming Interface) Music features 

Notes

Acknowledgment

This work has been partially financed by the Spain Government through the contract TIN2015-72241-EXP.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.ICESI UniversityCaliColombia
  2. 2.University of ZaragozaZaragozaSpain

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