A signal representation approach for discrimination between full and empty hazelnuts
Author
Onaran, İbrahim
İnce, N. F.
Tevfik, A. H.
Çetin, A. Enis
Date
2007Source Title
Proceedings of the 15th European Signal Processing Conference, EURASIP 2007
Print ISSN
2219-5491
Publisher
IEEE
Pages
2464 - 2468
Language
English
Type
Conference PaperItem Usage Stats
80
views
views
14
downloads
downloads
Abstract
We apply a sparse signal representation approach to impact acoustic signals to discriminate between empty and full hazelnuts. The impact acoustic signals are recorded by dropping the hazelnut shells on a metal plate. The impact signal is then approximated within a given error limit by choosing codevectors from a special dictionary. This dictionary was generated from sub-dictionaries that are individually generated for the impact signals corresponding to empty and full hazelnut. The number of codevectors selected from each sub-dictionary and the approximation error within initial codevectors are used as classification features and fed to a Linear Discriminant Analysis (LDA). We also compare this algorithm with a baseline approach. This baseline approach uses features which describe the time and frequency characteristics of the given signal that were previously used for empty and full hazelnut separation. Classification accuracies of 98.3% and 96.8% were achieved by the proposed approach and base algorithm respectively. The results we obtained show that sparse signal representation strategy can be used as an alternative classification method for undeveloped hazelnut separation with higher accuracies.
Keywords
Approximation errorsClassification accuracy
Classification features
Classification methods
Code vectors
Error limits
Frequency characteristic
Hazelnut shells
Impact acoustics
Impact signals
Linear discriminant analysis
Metal plates
Signal representations
Sparse signal representation
Acoustic waves
Algorithms
Signal processing
Separation
Permalink
http://hdl.handle.net/11693/27037Collections
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