Abstract
Vocal Separation is the separation of a set of source signals from a set of mixed signals, without extensive information about the source signals or the mixing process. Audio data is available in large amounts across the internet. Usage of machine learning and datasets constructed around one context can be used to design a system capable of performing vocal separation on audio data. Our algorithm uses a convolutional neural network, down sampling the input and then up sampling to a desired output. A custom dataset comprised of mixed conversations is used for training, with the expected output being the two vocal sources correctly separated.
Published Version
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