The 9th Annual MLSP Competition: First Place
Gábor Fodor

Abstract:
The goal of the 2013 MLSP Competition is to predict the set
of bird species present in audio recordings, collected in field
conditions. Real-world audio data presents special difficulties
such as simultaneously vocalizing birds, other animal sounds,
and background noise. Although the task can be considered
as a multi-instance multi-label learning problem, I propose a
Binary Relevance approach with Random Forest. The proposed
solution achieves 0.956 AUC and ranks 1st place on
the Kaggle private leaderboard.