6 research outputs found
Deep Remix: Remixing Musical Mixtures Using a Convolutional Deep Neural Network
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
Deep Karaoke: Extracting Vocals from Musical Mixtures Using a Convolutional Deep Neural Network
Identification and extraction of singing voice from within musical mixtures
is a key challenge in source separation and machine audition. Recently, deep
neural networks (DNN) have been used to estimate 'ideal' binary masks for
carefully controlled cocktail party speech separation problems. However, it is
not yet known whether these methods are capable of generalizing to the
discrimination of voice and non-voice in the context of musical mixtures. Here,
we trained a convolutional DNN (of around a billion parameters) to provide
probabilistic estimates of the ideal binary mask for separation of vocal sounds
from real-world musical mixtures. We contrast our DNN results with more
traditional linear methods. Our approach may be useful for automatic removal of
vocal sounds from musical mixtures for 'karaoke' type applications