| Title: |
Multi-label Learning Approaches for Music Instrument Recognition |
| Author(s): |
E. Spyromitros-Xioufis, G. Tsoumakas, I. Vlahavas.
|
| Availability: |
Click here to download the PDF (Acrobat Reader) file (10 pages).
|
| Keywords: |
Multi-label Learning, Instrument Recognition.
|
| Appeared in: |
Proc. 9th International Symposium on Methodologies for Intelligent Systems (ISMIS 2011), Warsaw, Poland, 2011.
|
| Abstract: |
This paper presents the two winning approaches that we developed
for the instrument recognition track of the ISMIS 2011 contest
on Music Information. The solution that ranked first was based on the
Binary Relevance approach and built a separate model for each instrument
on a selected subset of the available training data. Moreover, a
new ranking approach was utilized to produce an ordering of the instruments
according to their degree of relevance to a given track. The
solution that ranked second was based on the idea of constraining the
number of pairs that were being predicted. It applied a transformation
to the original dataset and utilized a variety of post-processing filters
based on domain knowledge and exploratory analysis of the evaluation
set. Both solutions were developed using the Mulan open-source software
for multi-label learning. |
| See also : |
ISMIS 2011 Contest: Music Information Retrieval
|
