Information Rate for Fast Time-Domain Instrument Classification

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9617)


In this paper, we propose a novel feature set for instrument classification which is based on the information rate of the signal in the time domain. The feature is extracted by calculating the Shannon entropy over a sliding short-time energy frame and binning statistical features into a unique feature vector. Experimental results are presented, including a comparison to frequency-domain feature sets. The proposed entropy features are shown to be faster than popular frequency-domain methods while maintaining comparable accuracy in an instrument classification task.


Audio classification Audio features Audio signal processing Time-domain methods 


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Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of ReginaReginaCanada

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