Pulsed Melodic Affective Processing: Musical structures for increasing transparency in emotional computation. (May 2014)

• Record Type: Journal Article
• Authors: Kirke, Alexis; Miranda, Eduardo
• Is Part Of: Simulation. Volume 90:Number 5(2014:May)
• Page Start: 606
• Page End: 622
• Publication Date: 2014-05
• Subjects: Communications -- human–computer interaction -- music -- affective computing -- Boolean logic -- neural networks -- emotions -- multi-agent systems -- robotics; Computer simulation -- Periodicals; 003.3
• DOI: 10.1177/0037549714531060
• Languages: English
• ISSNs: 0037-5497

Abstract

Pulsed Melodic Affective Processing (PMAP) is a method for the processing of artificial emotions in affective computing. PMAP is a data stream designed to be listened to, as well as computed with. The affective state is represented by numbers that are analogues of musical features, rather than by a binary stream. Previous affective computation has been done with emotion category indices, or real numbers representing various emotional dimensions. PMAP data can be generated directly by sound (e.g. heart rates or key-press speeds) and turned directly into music with minimal transformation. This is because PMAP data is music and computations done with PMAP data are computations done with music. This is important because PMAP is constructed so that the emotion that its data represents at the computational level will be similar to the emotion that a person "listening" to the PMAP melody hears. Thus, PMAP can be used to calculate "feelings" and the result data will "sound like" the feelings calculated. PMAP can be compared to neural spike streams, but ones in which pulse heights and rates encode affective information. This paper illustrates PMAP in a range of simulations. In a multi-agent simulation, initial results support that an affective multi-robot security system could use PMAP to provide a basic control mechanism for "search-and-destroy". Results of fitting a musical neural network with gradient descent to help solve a text emotional detection problem are also presented. The paper concludes by discussing how PMAP may be applicable in the stock markets, using a simplified order book simulation.