Papers › Exploring Isolated Musical Notes as Pre-training Data for Predominant Instrument...
Exploring Isolated Musical Notes as Pre-training Data for Predominant Instrument Recognition in Polyphonic Music
Lifan Zhong, Erica Cooper, Junichi Yamagishi, Nobuaki Minematsu
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
With the growing amount of musical data available, automatic instrument recognition, one of the essential problems in Music Information Retrieval (MIR), is drawing more and more attention. While automatic recognition of single instruments has been well-studied, it remains challenging for polyphonic, multi-instrument musical recordings. This work presents our efforts toward building a robust end-to-end instrument recognition system for polyphonic multi-instrument music. We train our model using a pre-training and fine-tuning approach: we use a large amount of monophonic musical data for pre-training and subsequently fine-tune the model for the polyphonic ensemble. In pre-training, we apply data augmentation techniques to alleviate the domain gap between monophonic musical data and real-world music. We evaluate our method on the IRMAS testing data, a polyphonic musical dataset comprising professionally-produced commercial music recordings. Experimental results show that 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 previous state-of-the-art end-to-end approach. Also, we are able to build a lightweight model, achieving competitive performance with only 519K trainable parameters.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections