There will be 2 presentations by our group at the ICML Machine Learning for Music Discovery Workshop (ML4MD) this year:
- Pati, Ashis; Lerch, Alexander: Latent Space Regularization for Explicit Control of Musical Attributes:
Deep generative models for music are often restrictive since they do not allow users any meaningful control over the generated music. To address this issue, we propose a novel latent space regularization technique which is capable of structuring the latent space of a deep generative model by encoding musically meaningful attributes along specific dimensions of the latent space. This, in turn, can provide users with explicit control over these attributes during inference and thereby, help design intuitive musical interfaces to enhance creative workflows.
- Gururani, Siddharth; Lerch, Alexander; Bretan, Mason: A Comparison of Music Input Domains for Self-Supervised Feature Learning:
In music using neural networks to learn effective feature spaces, or embeddings, that capture useful characteristics has been demonstrated in the symbolic and audio domains. In this work, we compare the symbolic and audio domains, attempting to identify the benefits of each, and whether incorporating both of the representations during learning has utility. We use a self-supervising siamese network to learn a low-dimensional representation of three second music clips and evaluate the learned features on their ability to perform a variety of music tasks. We use a polyphonic piano performance dataset and directly compare the performance on these tasks with embeddings derived from synthesized audio and the corresponding symbolic representations.