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Intelligent Audio Analysis pp 41–97Cite as

Audio Features

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Part of the book series: Signals and Communication Technology ((SCT))

Abstract

To represent the information contained in an audio (stream) in a compact way focussing on a task of interest, a parameterised form is usually chosen. These parameters describe properties of the audio usually in a highly information reduced form and typically at a considerably lower rate, such as the mean energy or pitch over a longer period of time. As different Intelligent Audio Analysis tasks are often best represented by different such ’features’, a broad selection of the most typical ones is presented. This includes description of the digitalisation and segmentation of the audio as first step. Features include intensity, zero-crossings, auto correlation, spectrum and cepstrum, linear prediction, line spectral pairs, perceptual linear prediction, formants, fundamental frequency and voicing probability, and jitter and shimmer from the speech domain. Further, music, sound, and textual descriptors are included. Then, the principle of supra-segmental brute-forcing and subsequent reduction and selection are explained. As an example serves the widely used openSMILE feature extractor.

The ability to focus attention on important things is a defining characteristic of intelligence.

—Robert J. Shiller.

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Notes

  1. 1.

    ISO/IEC JTC 1/SC 29/WG 11 N7708.

  2. 2.

    \((VC)^m\) here means an \(m\)-fold repetition of the string \(VC\)

  3. 3.

    http://conceptnet.media.mit.edu/

  4. 4.

    http://commons.media.mit.edu/en/

  5. 5.

    openNLP notation is followed for POS classes.

  6. 6.

    Available at: http://opensmile.sourceforge.net/.

  7. 7.

    http://www.phon.ucl.ac.uk/resource/sfs/

  8. 8.

    http://cobweb.ecn.purdue.edu/malcolm/interval/1998-010/

  9. 9.

    http://affect.media.mit.edu/publications.php

  10. 10.

    http://www.speech.kth.se/snack/

  11. 11.

    http://libxtract.sourceforge.net/

  12. 12.

    http://marsyas.sness.net/

  13. 13.

    https://www.jyu.fi/hum/laitokset/musiikki/en/research/coe/materials/mirtoolbox

  14. 14.

    A more detailed description can be found in the openSMILE documentation available in the download package at http://sourceforge.net/projects/opensmile/.

  15. 15.

    openSMILE was awarded third place in the ACM Multimedia 2010 Open-Source Software Competition. It was further used as standard feature extractor for baseline computation and use by participants in six research challenges.

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  1. LS für Mensch-Maschine-Kommunikation, TU München, Arcisstr. 21, München, 80290, Germany

    Björn Schuller

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Schuller, B. (2013). Audio Features. In: Intelligent Audio Analysis. Signals and Communication Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36806-6_6

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  • DOI: https://doi.org/10.1007/978-3-642-36806-6_6

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