Boosting audio chord estimation using multiple classifiers

Matevz Pesek, Ales Leonardis, Matija Marolt

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

Abstract

The paper addresses the task of automatic audio chord estimation using stacked generalization of multiple classifiers over Hidden Markov model (HMM) estimators. We evaluated two feature types for chord estimation: a new compositional hierarchical model and standard chroma feature vectors. The compositional hierarchical model is presented as an alternative deep learning approach. Both feature types are further modelled with two separate Hidden Markov models (HMMs) in order to estimate chords in music recordings. Further, a binary decision tree and support vector machine are proposed binding the HMM estimations into a new feature vector. The additional stacking of the classifiers provides a classification boost by 17.55% with a binary decision tree and and 21.96% using the support vector machine.

Original languageEnglish
Title of host publication21st International Conference on Systems, Signals, and Image Processing
PublisherIEEE Computer Society Press
Pages107-110
Number of pages4
ISBN (Print)9789531841917
Publication statusPublished - 2014
Event21st International Conference on Systems, Signals and Image Processing, IWSSIP 2014 - Dubrovnik, Croatia
Duration: 12 May 201415 May 2014

Conference

Conference21st International Conference on Systems, Signals and Image Processing, IWSSIP 2014
Country/TerritoryCroatia
CityDubrovnik
Period12/05/1415/05/14

Keywords

  • audio chord estimation
  • compositional hierarchical model
  • deep learning
  • stacking generalization

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Signal Processing
  • Software

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