Model Selection for Production System via Automated Online Experiments
Zhenwen Dai, Praveen Chandar, Ghazal Fazelnia, Benjamin Carterette, Mounia Lalmas
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
Zhenwen Dai, Praveen Chandar, Ghazal Fazelnia, Benjamin Carterette, Mounia Lalmas
Federico Tomasi, Rishabh Mehrotra, Aasish Pappu, Judith Bütepage, Brian Brost, Hugo Galvão, Mounia Lalmas
Casper Hansen, Christian Hansen, Lucas Maystre, Rishabh Mehrotra, Brian Brost, Federico Tomasi, Mounia Lalmas