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Intelligent Audio Analysis pp 99–138Cite as

Audio Recognition

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

Abstract

A huge variety of learning algorithms is applied in the field of Intelligent Audio Analysis depending on the nature of the target task of interest such as being static or dynamic. In addition, manifold other factors are decisive when selecting the optimal algorithm incorporating aspects such as efficiency or reliability. The most frequently found representatives are explained in detail: Decision Trees, Support Vector-based approaches, Artificial Neural Networks including the Long Short-Term Memory paradigm, as well as dynamic modelling by hidden Markov models. In addition, bootstrapping, meta-learning, and tandem learning are described. This is followed by the optimal evaluation of such algorithms touching partitioning and balancing, and evaluation measures as are frequently used in the field.

Learning without thought is labor lost; thought without learning is perilous.

—Confucius.

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Notes

  1. 1.

    Note that for better readability, the time \(t\) is in this section used in the subscript or argument following [32].

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

    Björn Schuller

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  1. Björn Schuller
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Schuller, B. (2013). Audio Recognition. In: Intelligent Audio Analysis. Signals and Communication Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36806-6_7

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  • DOI: https://doi.org/10.1007/978-3-642-36806-6_7

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