Hello, and welcome to the Paper of the Day (Po’D): Music genre classification via compressive sampling edition.
Today’s paper is: B. L. Sturm, “On Music genre classification via compressive sampling“, Proc. ICME 2013.
This paper is the closing chapter on the findings reported in K. Chang, J.-S. R. Jang, and C. S. Iliopoulos, “Music genre classification via compressive sampling,” in Proc. Int. Soc. Music Information Retrieval, (Amsterdam, the Netherlands), pp. 387-392, Aug. 2010.
The one-line summary of my paper, for those in a hurry: Results contradicting two well-supported findings of machine learning and music information research? We show the contradictions are not real.
I first discussed the work of Chang et al. here; and then two years later discussed several issues with the work, and finally reproduced it and submitted a paper with my code.
My paper is now accepted and revised with many changes suggested by the helpful reviews. This is my third negative results paper (the first is here, the second here).
I must take care to not become too negative!
Anyhow, it is quite satisfying to receive the following reviewer comment on my paper:
The paper provides extremely reproducible results that help to clear the confusion caused by previous works. The result is consistent with other works which show that compressive sampling / random projection reduce classification accuracy. Classification research is heavily directed by the top performers in the field. In this case, the authors address the failings of previous authors to sufficiently explain their methods. Without papers such as this one, the field continues to be muddied by works that claim inflated results without providing sufficient data to reproduce their work, and researchers waste time chasing phantom results. I applaud the rigor with which the research was performed and explained.