Musical instrument recognition by pairwise classification strategies

Musical instrument recognition is an important aspect of music information retrieval. In this paper, statistical pattern recognition techniques are utilized to tackle the problem in the context of solo musical phrases. Ten instrument classes from different instrument families are considered. A large sound database is collected from excerpts of musical phrases acquired from commercial recordings translating different instrument instances, performers, and recording conditions. More than 150 signal processing features are studied including new descriptors. Two feature selection techniques, inertia ratio maximization with feature space projection and genetic algorithms are considered in a class pairwise manner whereby the most relevant features are fetched for each instrument pair. For the classification task, experimental results are provided using Gaussian mixture models (GMMs) and support vector machines (SVMs). It is shown that higher recognition rates can be reached with pairwise optimized subsets of features in association with SVM classification using a radial basis function kernel