Nonnegative Tensor Factorization for Source Separation of Loops in Audio

• Published in: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 171 - 175
• Main Authors: Smith, Jordan B. L., Goto, Masataka
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-04-2018
• Subjects: Iterative methods; Layout; loop-based music; Measurement; nonnegative tensor factorization; repetition; Rhythm; Source separation; Spectrogram; Tensile stress
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP.2018.8461876

The prevalence of exact repetition in loop-based music makes it an opportune target for source separation. Nonnegative factorization approaches have been used to model the repetition of looped content, and kernel additive modeling has leveraged periodicity within a piece to separate looped background elements. We propose a novel method of leveraging periodicity in a factorization model: we treat the two-dimensional spectrogram as a three-dimensional tensor, and use nonnegative tensor factorization to estimate the component spectral templates, rhythms and loop recurrences in a single step. Testing our method on synthesized loop-based examples, we find that our algorithm mostly exceeds the performance of competing methods, with a reduction in execution cost. We discuss limitations of the algorithm as we demonstrate its potential to analyze larger and more complex songs.