Information rate for fast time-domain instrument classification

Abstract

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.