Improving Bass Saliency Estimation using Transfer Learning and Label Propagation
In this paper, we consider two methods to improve an algorithm for bass saliency estimation in jazz ensemble recordings which are based on deep neural networks. First, we apply label propagation to increase the amount of training data by transferring pitch labels from our labeled dataset to unlabeled audio recordings using a spectral similarity measure. Second, we study in several transfer learning experiments, whether isolated note recordings can be beneficial for pre-training a model which is later fine-tuned on ensemble recordings. Our results indicate that both strategies can improve the performance on bass saliency estimation by up to five percent in accuracy.