Guitar model recognition from single instrument audio recordings

Abstract

The main goal of this paper is to explore the recognition of particular guitar models from single instrument audio recordings. This is different than existing work in music instrument recognition that deals with identifying different instrument types. Through a set of experiments we evaluate different sets of audio features and classifiers for this purpose. To improve accuracy a composite classifier is implemented to first discriminate between electric and acoustic guitars. This affords flexibility in training different models for each guitar type. A data set consisting of audio recordings from 15 guitar models, each recorded with a set of different playing configurations, is used for training and testing. We have found that K Nearest Neighbors and Support Vector Machine (SVM) classifiers perform the best. Testing is done by leaving a specific playing configuration out of the training model. Specific test cases show satisfactory results, with one test case achieving over 70% accuracy and a second one over 50%; both using a composite SVM model.