Feature-Informed Latent Space Regularization for Music Source Separation

Yun-Ning Hung; Alexander Lerch
DAFx-2022 - Vienna
The integration of additional side information to improve music source separation has been investigated numerous times, e.g., by adding features to the input or by adding learning targets in a multi-task learning scenario. These approaches, however, require additional annotations such as musical scores, instrument labels, etc. in training and possibly during inference. The available datasets for source separation do not usually provide these additional annotations. In this work, we explore transfer learning strategies to incorporate VGGish features with a state-of-the-art source separation model; VGGish features are known to be a very condensed representation of audio content and have been successfully used in many music information retrieval tasks. We introduce three approaches to incorporate the features, including two latent space regularization methods and one naive concatenation method. Our preliminary results show that our proposed approaches could improve some evaluation metrics for music source separation. In this work, we also include a discussion of our proposed approaches, such as the pros and cons of each approach, and the potential extension/improvement.