Cross-Dataset Validation of Feature Sets in Musical Instrument Classification

Abstract—Automatically identifying the musical instruments present in audio recordings is a complex and difficult task. Although the focus has recently shifted to identifying instruments in a polyphonic setting, the task of identifying solo instruments has not been solved. Most empirical studies recognizing musical instruments use only a single dataset in the experiments, despite evidence that mapproaches do not generalize from one dataset to another dataset. In this work, we present a method for data-driven learning of spectral filters for use in feature extraction from audio recordings of solo musical instruments and discuss the extensibility of this approach to polyphonic mixtures of instruments. We examine four datasets of musical instrument sounds that have 13 instruments in common. We demonstrate cross-dataset validation by showing that a feature extraction scheme learned from one dataset can be used successfully for feature extraction and classification on another dataset. Keywords—binary relevance classification, classification, cross-dataset validation, feature extraction, k-nearest neighbor, instru-ment recognition, machine learning, music, music information retrieval, musical note separation, timbre I