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Standard Baseline Feature Sets

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Real-time Speech and Music Classification by Large Audio Feature Space Extraction

Part of the book series: Springer Theses ((Springer Theses))

Abstract

A central aim of this thesis was to define standard acoustic feature sets for both speech and music, which contain a large and comprehensive set of acoustic descriptors. Based on previous efforts to combine features and the authors experience from evaluations across several databases and tasks, 12 standard acoustic parameter sets have been proposed and well evaluated for this thesis. These sets include the acoustic baseline features sets of the INTERSPEECH challenges on Emotion and Paralinguistics form 2009–2013 (ComParE) as well as the Audio-Visual Emotion Challenges (2011–2013). Further, two sets for music processing and two minimalistic speech parameter sets (GeMAPS and eGeMAPS) are proposed.

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Notes

  1. 1.

    An article is to appear in IEEE Transactions on Affective Computing (Eyben et al. 2015).

  2. 2.

    According to Schuller et al. (2011a)—and the openSMILE configuration file—the IS11 set contains 4,368 features in total. This is also the size of the baseline feature vectors provided for the challenge. However, the duration of the segment is counted twice there, due to the way it was implemented in the openSMILE configuration file. Thus, the correct number of unique features in IS11 is 4,367.

  3. 3.

    The RTF for the rhythmic features was not evaluated, as they are not implemented in the openSMILE framework.

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Authors and Affiliations

  1. Institute for Human-Machine Communication (MMK), Technische Universität München, Munich, Germany

    Florian Eyben

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  1. Florian Eyben
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Correspondence to Florian Eyben .

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Eyben, F. (2016). Standard Baseline Feature Sets. In: Real-time Speech and Music Classification by Large Audio Feature Space Extraction. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-27299-3_3

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  • DOI: https://doi.org/10.1007/978-3-319-27299-3_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27298-6

  • Online ISBN: 978-3-319-27299-3

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