preprint · 2015

KERNEL SPECTROGRAM MODELS FOR SOURCE SEPARATION

Cited by 11Abstract and details only

Abstract

In this study, we introduce a new framework called Kernel Additive Modelling for audio spectrograms that can be used for multichannel source separation. It assumes that the spectrogram of a source at any time-frequency bin is close to its value in a neighbourhood indicated by a source-specific proximity kernel. The rationale for this model is to easily account for features like periodicity, stability over time or frequency, self-similarity, etc. In many cases, such local dynamics are indeed much more natural to assess than any global model such as a tensor factorization. This framework permits one to use different proximity kernels for different sources and to estimate them blindly using their mixtures only. Estimation is performed using a variant of the kernel backfitting algorithm that allows for multichannel mixtures and permits parallelization. Experimental results on the separation of vocals from musical backgrounds demonstrate the efficiency of the approach. Index Terms—audio source separation, spatial filtering, spec-trogram models I.

Cite this paper

APA 7

(2015). KERNEL SPECTROGRAM MODELS FOR SOURCE SEPARATION.

MLA 9

“KERNEL SPECTROGRAM MODELS FOR SOURCE SEPARATION.” 2015.

Chicago

2015. “KERNEL SPECTROGRAM MODELS FOR SOURCE SEPARATION.”

Harvard

(2015) ‘KERNEL SPECTROGRAM MODELS FOR SOURCE SEPARATION’.

BibTeX

@misc{anon2015kernel,
  title = {{KERNEL SPECTROGRAM MODELS FOR SOURCE SEPARATION}},
  year = {2015}
}

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