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Deep Remix: Remixing Musical Mixtures Using a Convolutional Deep Neural Network

Simpson, AJR, Roma, G and Plumbley, MD (2015) Deep Remix: Remixing Musical Mixtures Using a Convolutional Deep Neural Network

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Abstract

Audio source separation is a difficult machine learning problem and performance is measured by comparing extracted signals with the component source signals. However, if separation is motivated by the ultimate goal of re-mixing then complete separation is not necessary and hence separation difficulty and separation quality are dependent on the nature of the re-mix. Here, we use a convolutional deep neural network (DNN), trained to estimate 'ideal' binary masks for separating voice from music, to perform re-mixing of the vocal balance by operating directly on the individual magnitude components of the musical mixture spectrogram. Our results demonstrate that small changes in vocal gain may be applied with very little distortion to the ultimate re-mix. Our method may be useful for re-mixing existing mixes.

Item Type: Article
Authors :
NameEmailORCID
Simpson, AJRUNSPECIFIEDUNSPECIFIED
Roma, GUNSPECIFIEDUNSPECIFIED
Plumbley, MDm.plumbley@surrey.ac.ukUNSPECIFIED
Date : 1 May 2015
Uncontrolled Keywords : cs.SD, cs.SD, 68Txx
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 13:37
Last Modified : 17 May 2017 15:12
URI: http://epubs.surrey.ac.uk/id/eprint/839879

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