The way humans listen to music and perceive its structure isautomatic. In an attempt by Friberg et al. (2011) to model thishuman perception mechanism, a set of nine perceptual features wasselected to describe the overall properties of music. By letting atest group rate the perceptual features in a data set of musicalpieces, they discovered that the factor with most importance fordescribing the emotions happy and sad was the perceptual featuremodality. Modality in music denotes whether the key of a musicalpiece is in major or minor.This thesis aims to predict the modality in a continuous scale (0-10) from chord analysis with multiple linear regression and a NeuralNetwork (NN) in a computational model using a custom set offeatures. The model was able to predict the modality with anexplained variability of 64 % using a NN. The results clearlyindicated that the approach of using chords as features to predictmodality, is appropriate for music data sets that consisted of tonalmusic. / Computational Modelling of Perceptual Music Features
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-189437 |
| Date | January 2016 |
| Creators | Eriksson, Jens |
| Publisher | KTH, Tal-kommunikation |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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