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Erschienen in:
Advances in Feature Selection for Data and Pattern Recognition: An Introduction Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

13. Application of Tolerance Near Sets to Audio Signal Classification

verfasst von : Ashmeet Singh, Sheela Ramanna

Erschienen in: Advances in Feature Selection for Data and Pattern Recognition

Verlag: Springer International Publishing

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Abstract

This chapter is an extension of our work presented where the problem of classifying audio signals using a supervised tolerance class learning algorithm (TCL) based on tolerance near sets was first proposed. In the tolerance near set method(TNS), tolerance classes are directly induced from the data set using a tolerance level and a distance function. The TNS method lends itself to applications where features are real-valued such as image data, audio and video signal data. Extensive experimentation with different audio-video data sets were performed to provide insights into the strengths and weaknesses of the TCL algorithm compared to granular (fuzzy and rough) and classical machine learning algorithms.

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Metadaten
Titel
Application of Tolerance Near Sets to Audio Signal Classification
verfasst von
Ashmeet Singh
Sheela Ramanna
Copyright-Jahr
2018
Buch
Advances in Feature Selection for Data and Pattern Recognition

Print ISBN: 978-3-319-67587-9

Electronic ISBN: 978-3-319-67588-6

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-67588-6_13