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Intelligent Audio Analysis pp 149–165Cite as

Audio Enhancement and Robustness

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

Abstract

Once an audio recognition system that functions under idealistic conditions is established, the primary concern shifts towards making it robust in the real-world. Several options exist for system improvement along the chain of processing, and have proved to be promising especially in the monaural case. Here, most frequently methods and some recent candidates are explained, first including advanced front-end feature extraction, unsupervised spectral subtraction, feature enhancement and normalisation by Cepstral Mean Subtraction, Mean and Variance Normalisation, and Histogram Equalisation. Then, model-based feature enhancement based on (switching) linear dynamical modelling is followed by model architectures such as (hidden) conditional random fields, and switching autoregressive approaches.

Our view\((\dots )\)is that it is an essential characteristic of experimentation that it is carried out with limited resources, and an essential part of the subject of experimental design to ascertain how these should be best applied; or, in particular, to which causes of disturbance care should be given, and which ought to be deliberately ignored. —Sir Ronald A. Fisher.

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

    Björn Schuller

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  1. Björn Schuller
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Schuller, B. (2013). Audio Enhancement and Robustness. In: Intelligent Audio Analysis. Signals and Communication Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36806-6_9

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  • DOI: https://doi.org/10.1007/978-3-642-36806-6_9

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