Learning Complex Basis Functions for Invariant Representations of Audio

Understand

Learning features from data has shown to be more successful than using hand-crafted features for many machine learning tasks.

• In music information retrieval (MIR), features learned from windowed spectrograms are highly variant to transformations like transposition or time-shift.
• Such variances are undesirable when they are irrelevant for the respective MIR task.
• We propose an architecture called Complex Autoencoder (CAE) which learns features invariant to orthogonal transformations.