This paper addresses feature extraction for automatic chord recognition systems. Most chord recognition systems use chroma features as a front-end and some kind of classifier (HMM, SVM or template matching). The vast majority of feature extraction approaches are based on mapping frequency bins from spectrum or constant-Q spectrum to chroma bins. In this work a set of new chroma features that are based on the time-frequency reassignment (TFR) technique is investigated. The proposed feature set was evaluated on the commonly used Beatles dataset and proved to be efficient for the chord recognition task, outperforming standard chroma.

Time-frequency reassigned features for automatic chord recognition

Khadkevich, Maksim;Omologo, Maurizio
2011

Abstract

This paper addresses feature extraction for automatic chord recognition systems. Most chord recognition systems use chroma features as a front-end and some kind of classifier (HMM, SVM or template matching). The vast majority of feature extraction approaches are based on mapping frequency bins from spectrum or constant-Q spectrum to chroma bins. In this work a set of new chroma features that are based on the time-frequency reassignment (TFR) technique is investigated. The proposed feature set was evaluated on the commonly used Beatles dataset and proved to be efficient for the chord recognition task, outperforming standard chroma.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11582/29712
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
social impact