Identification of instruments in polyphonic recordings is a challenging, but fundamental problem in music information retrieval. While there has been significant progress in developing predictive models for this and related classification tasks, we as a community lack a common data-set which is large, freely available, diverse, and representative of naturally occurring recordings. This limits our ability to measure the efficacy of computational models. This article describes the construction of a new, open data-set for multi-instrument recognition. The dataset contains 20,000 examples of Creative Commons-licensed music available on the Free Music Archive. Each example is a 10-second excerpt which has been partially labeled for the presence or absence of 20 instrument classes by annotators on a crowd-sourcing platform. We describe in detail how the instrument taxonomy was constructed, how the dataset was sampled and annotated, and compare its characteristics to similar, previous data-sets. Finally, we present experimental results and baseline model performance to motivate future work
Marco De Nadai, Francesco Fabbri, Paul Gigioli, Alice Wang, Ang Li, Fabrizio Silvestri, Laura Kim, Shawn Lin, Vladan Radosavljevic, Sandeep Ghael, David Nyhan, Hugues Bouchard, Mounia Lalmas-Roelleke, Andreas Damianou
Andreas Damianou, Francesco Fabbri, Paul Gigioli, Marco De Nadai, Alice Wang, Enrico Palumbo, Mounia Lalmas
Zhenwen Dai, Federico Tomasi, Sina Ghiassian
