Music Genre Classification using an Auditory Memory Model

Kristoffer Jensen

Publikation: Bidrag til bog/antologi/rapport/konference proceedingBidrag til bog/antologiForskningpeer review

2 Citationer (Scopus)
406 Downloads (Pure)

Abstrakt

Audio feature estimation is potentially improved by including higher- level models. One such model is the Auditory Short Term Memory (STM) model. A new paradigm of audio feature estimation is obtained by adding the influence of notes in the STM. These notes are identified when the perceptual spectral flux has a peak, and the spectral content that is increased by the new note is added to the STM. The STM is exponentially fading with time span and number of elements, and each note only belongs to the STM for a limited time. Initial experiment regarding the behavior of the STM shows promising results, and an initial experiment with sensory dissonance has been undertaken with good results. The parameters obtained form the auditory memory model, along with the dissonance measure, are shown here to be of interest in genre classification.
OriginalsprogEngelsk
TitelSpeech, sound and music processing: Embracing research in India : 8th International Symposium, CMMR 2011, 20th International Symposium, FRSM 2011, Bhubaneswar, India, March 9-12, 2011, Revised Selected Papers
RedaktørerSølvi Ystad, Mitsuko Aramaki, Richard Kronland-Martinet, Kristoffer Jensen, Sanghamitra Mohanty
Antal sider12
Vol/bind7172
UdgivelsesstedBerlin
ForlagSpringer Science+Business Media
Publikationsdato2012
Sider79-88
ISBN (Trykt)978-3-642-31979-2
ISBN (Elektronisk)978-3-642-31980-8
DOI
StatusUdgivet - 2012
NavnLecture Notes in Computer Science
ISSN0302-9743

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