Extending Non-Negative Matrix Factorization to Irregularly-Sampled Time-Frequency Representations using Implicit Neural Representations

Non-negative matrix factorization (NMF) can be extended to irregularly-sampled time-frequency representations by formulating it in terms of continuous functions instead of fixed vectors, enabling the use of implicit neural representations to model the underlying basis templates and activations.