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Tolerance-Based Approach to Audio Signal Classification

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Advances in Artificial Intelligence (Canadian AI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9673))

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Abstract

In this paper, we propose a supervised learning algorithm to classify audio signals based on the tolerance near sets model (TNS). In the TNS method, tolerance classes are directly induced from the data set using the tolerance level \(\varepsilon \) and a distance function. Preliminary experiments with an audio signal data set show promising results in terms of the accuracy of classifier. Overall, TCL is able to demonstrate similar performance in terms of accuracy with Fuzzy IDT algorithm [1] and comparable performance with a rough set based classifier as well as classical machine learning algorithms based on decision trees, rules, bayesian learning and support vector machines.

This research has been supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery grant 194376. Special thanks to Dr. Rajen Bhatt, Robert Bosch Technology Research Center, US for sharing this data set.

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Notes

  1. 1.

    http://www.cs.waikato.ac.nz/ml/weka/.

  2. 2.

    http://www.mimuw.edu.pl/~szczuka/rses/start.html.

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Author information

Authors and Affiliations

  1. Department of Applied Computer Science, University of Winnipeg, Winnipeg, MB, R3B 2E9, Canada

    Sheela Ramanna & Ashmeet Singh

Authors
  1. Sheela Ramanna
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  2. Ashmeet Singh
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Corresponding author

Correspondence to Sheela Ramanna .

Editor information

Editors and Affiliations

  1. Lakehead University, Thunder Bay, Ontario, Canada

    Richard Khoury

  2. National Research Council Canada , Ottawa, Canada

    Christopher Drummond

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Ramanna, S., Singh, A. (2016). Tolerance-Based Approach to Audio Signal Classification. In: Khoury, R., Drummond, C. (eds) Advances in Artificial Intelligence. Canadian AI 2016. Lecture Notes in Computer Science(), vol 9673. Springer, Cham. https://doi.org/10.1007/978-3-319-34111-8_11

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  • DOI: https://doi.org/10.1007/978-3-319-34111-8_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-34110-1

  • Online ISBN: 978-3-319-34111-8

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