Remixing musical audio on the web using source separation
Author(s)
Roma, Gerard
Simpson, Andrew J.R.
Grais, Emad M.
Plumbley, Mark D.
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Abstract
Research in audio source separation has progressed a long
way, producing systems that are able to approximate the
component signals of sound mixtures. In recent years, many
efforts have focused on learning time-frequency masks that can be used to filter a monophonic signal in the frequency domain. Using current web audio technologies, time-frequency
masking can be implemented in a web browser in real time.
This allows applying source separation techniques to arbitrary
audio streams, such as internet radios, depending on
cross-domain security configurations. While producing good
quality separated audio from monophonic music mixtures is still challenging, current methods can be applied to remixing
scenarios, where part of the signal is emphasized or deemphasized.
This paper describes a system for remixing
musical audio on the web by applying time-frequency masks
estimated using deep neural networks. Our example prototype,
implemented in client-side Javascript, provides reasonable
quality results for small modifications.
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Date
2016-04
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Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).