LEVERAGING NOISY ONLINE DATABASES FOR USE IN CHORD RECOGNITION

The most significant problem faced by Machine Learningbased chord recognition systems is arguably the lack of highquality training examples. In this paper, we address this problem by leveraging the availability of chord annotations from guitarist websites. We show that such annotations can be used as partial supervision of a semi-supervised chord recognition method—partial since accurate timing information is lacking. A particular challenge in the exploitation of these data is their low quality, potentially even leading to a performance degradation if used directly. We demonstrate however that a curriculum learning strategy can be used to automatically rank annotations according to their potential for improving the performance. Using this strategy, our experiments show a modest improvement for a simple major/minor chord alphabet, but a highly significant improvement for a much larger chord alphabet. 1