are the concepts, based on musical theory, music perception or signal processing, that are used to analyze, describe or transform a piece of music. Because of that, they constitute the building blocks of any Music Information Retrieval system. They can be global for a given piece of music (e.g., key or tonality), or can be time-varying (e.g., energy). Musical features have numerical or textual values associated. Their similarities and differences make possible to build predictive models of more complex or composite features, in a hierarchical way.
Published in Chapter:
Automatic Detection of Emotion in Music: Interaction with Emotionally Sensitive Machines
Cyril Laurier (Universitat Pompeu Fabra, Spain) and Perfecto Herrera (Universitat Pompeu Fabra, Spain)
Copyright: © 2009
|Pages: 25
DOI: 10.4018/978-1-60566-354-8.ch002
Abstract
Creating emotionally sensitive machines will significantly enhance the interaction between humans and machines. In this chapter we focus on enabling this ability for music. Music is extremely powerful to induce emotions. If machines can somehow apprehend emotions in music, it gives them a relevant competence to communicate with humans. In this chapter we review the theories of music and emotions. We detail different representations of musical emotions from the literature, together with related musical features. Then, we focus on techniques to detect the emotion in music from audio content. As a proof of concept, we detail a machine learning method to build such a system. We also review the current state of the art results, provide evaluations and give some insights into the possible applications and future trends of these techniques.