Multi-objective evolutionary feature selection for instrument recognition in polyphonic audio mixtures

Instrument recognition is one of the music information retrieval research topics. This task becomes very challenging if several instruments are played simultaneously because of their varying physical characteristics: inharmonic attack noise, energy development during attack–decay–sustain–release envelope or overtone distribution. In our framework, we treat instrument detection as a machine-learning task based on a large amount of preprocessed audio features with target to build classification models. Since classification algorithms are very sensitive to feature input and the optimal feature set differs from instrument to instrument, we propose to run a multiobjective feature selection procedure before building of classification models. Two objectives are considered for evaluation: classification mean-squared error and feature rate (smaller amount of features stands for reduced costs and decreased risk of overfitting). The analysis of the extensive experimental study confirms that application of an evolutionary multi-objective algorithm is a good choice to optimize feature selection for music instrument identification.