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Chord and modality analysis
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

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.

Place, publisher, year, edition, pages
2016. , 46 p.
Keyword [en]
Chord analysis, neural network
National Category
Signal Processing
URN: urn:nbn:se:kth:diva-189437OAI: diva2:946027
Educational program
Master of Science in Engineering - Electrical Engineering
Computational Modelling of Perceptual Music Features
Available from: 2016-07-05 Created: 2016-07-04 Last updated: 2016-07-05Bibliographically approved

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