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Comparing Probabilistic Models for Melodic Sequences

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationMACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, PT III
EditorsD Gunopulos, T Hofmann, D Malerba, M Vazirgiannis
Place of PublicationBERLIN
PublisherSpringer-Verlag Berlin Heidelberg
Pages289-304
Number of pages16
ISBN (Electronic)978-3-642-23808-6
ISBN (Print)978-3-642-23807-9
DOIs
Publication statusPublished - 2011
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) - Athens, Greece
Duration: 5 Sep 20119 Sep 2011

Publication series

NameLecture Notes in Artificial Intelligence
PublisherSPRINGER-VERLAG BERLIN
Volume6913
ISSN (Print)0302-9743

Conference

ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)
CountryGreece
Period5/09/119/09/11

Abstract

Modelling the real world complexity of music is a challenge for machine learning. We address the task of modeling melodic sequences from the same music genre. We perform a comparative analysis of two probabilistic models; a Dirichlet Variable Length Markov Model (Dirichlet-VMM) and a Time Convolutional Restricted Boltzmann Machine (TC-RBM). We show that the TC-RBM learns descriptive music features, such as underlying chords and typical melody transitions and dynamics. We assess the models for future prediction and compare their performance to a VMM, which is the current state of the art in melody generation. We show that both models perform significantly better than the VMM, with the Dirichlet-VMM marginally outperforming the TC-RBM. Finally, we evaluate the short order statistics of the models, using the Kullback-Leibler divergence between test sequences and model samples, and show that our proposed methods match the statistics of the music genre significantly better than the VMM.

    Research areas

  • melody modeling, music feature extraction, time convolutional restricted Boltzmann machine, variable length Markov model, Dirichlet prior, LEARNING ALGORITHM

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