2 research outputs found
Shift-Invariant Kernel Additive Modelling for Audio Source Separation
A major goal in blind source separation to identify and separate sources is
to model their inherent characteristics. While most state-of-the-art approaches
are supervised methods trained on large datasets, interest in non-data-driven
approaches such as Kernel Additive Modelling (KAM) remains high due to their
interpretability and adaptability. KAM performs the separation of a given
source applying robust statistics on the time-frequency bins selected by a
source-specific kernel function, commonly the K-NN function. This choice
assumes that the source of interest repeats in both time and frequency. In
practice, this assumption does not always hold. Therefore, we introduce a
shift-invariant kernel function capable of identifying similar spectral content
even under frequency shifts. This way, we can considerably increase the amount
of suitable sound material available to the robust statistics. While this leads
to an increase in separation performance, a basic formulation, however, is
computationally expensive. Therefore, we additionally present acceleration
techniques that lower the overall computational complexity.Comment: Feedback is welcom