AES E-Library

On the Importance of Temporal Context in Proximity Kernels: A Vocal Separation Case Study

Musical source separation methods exploit source-specific spectral characteristics to facilitate the decomposition process. Kernel Additive Modelling (KAM) models a source applying robust statistics to time-frequency bins as specified by a source-specific kernel, a function defining similarity between bins. Kernels in existing approaches are typically defined using metrics between single time frames. In the presence of noise and other sound sources information from a single-frame, however, turns out to be unreliable and often incorrect frames are selected as similar. In this paper, we incorporate a temporal context into the kernel to provide additional information stabilizing the similarity search. Evaluated in the context of vocal separation, our simple extension led to a considerable improvement in separation quality compared to previous kernels.

 

Author (s):
Affiliation: (See document for exact affiliation information.)
Publication Date:
Session subject:
Permalink: https://aes2.org/publications/elibrary-page/?id=18752


(583KB)


Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member Join the AES. If you need to check your member status, login to the Member Portal.

Type:
E-Libary location:
16938
Choose your country of residence from this list:










Skip to content