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With the growing amount of musical data available, automatic instrumentrecognition, one of the essential problems in Music Information Retrieval(MIR), is drawing more and more attention. While automatic recognition ofsingle instruments has been well-studied, it remains challenging forpolyphonic, multi-instrument musical recordings. This work presents our effortstoward building a robust end-to-end instrument recognition system forpolyphonic multi-instrument music. We train our model using a pre-training andfine-tuning approach: we use a large amount of monophonic musical data forpre-training and subsequently fine-tune the model for the polyphonic ensemble.In pre-training, we apply data augmentation techniques to alleviate the domaingap between monophonic musical data and real-world music. We evaluate ourmethod on the IRMAS testing data, a polyphonic musical dataset comprisingprofessionally-produced commercial music recordings. Experimental results showthat our best model achieves a micro F1-score of 0.674 and an LRAP of 0.814,meaning 10.9% and 8.9% relative improvement compared with the previousstate-of-the-art end-to-end approach. Also, we are able to build a lightweightmodel, achieving competitive performance with only 519K trainable parameters.

Exploring Isolated Musical Notes as Pre-training Data for Predominant Instrument Recognition in Polyphonic Music