Stratification of String Instruments Using Chroma-Based Features

Abstract

Identification of instrument type from acoustic signal is a challenging issue. It is also an interesting and popular research area having several promising applications in music industry. Researchers have already been able to classify instruments into several broad categories like String, Woodwind, Percussion, and Keyboard etc. using acoustic features like Mel-Frequency Cepstral Coefficients (MFCCs), Zero Crossing Rate (ZCR) etc. MFCC has been found to be excessively used. In this work an alternative acoustic feature of MFCC has been proposed. Chroma is an octave independent estimation of strength of all possible notes in Western 12 note scale at different points of time. Sound envelope originated by a note reflects the signature of an instrument and this can be used to stratify String instruments into various categories. The proposed work relies on Chroma-based low-dimensional feature vector to categorize String instruments. For classification purpose, simple and popular classifiers like Neural Network, k-NN, Naive Bayes’ have been exercised.