@inbook{6adfca7bb49443feb7fafd7040737beb,
title = "Music Genre Classification using an Auditory Memory Model",
abstract = "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.",
author = "Kristoffer Jensen",
year = "2012",
doi = "10.1007/978-3-642-31980-8_7",
language = "English",
isbn = "978-3-642-31979-2",
volume = "7172",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science+Business Media",
pages = "79--88",
editor = "S{\o}lvi Ystad and Mitsuko Aramaki and Richard Kronland-Martinet and Kristoffer Jensen and Sanghamitra Mohanty",
booktitle = "Speech, sound and music processing: Embracing research in India",
address = "United States",
}