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arXiv · 1705.02514

End-to-end Source Separation with Adaptive Front-Ends

Shrikant Venkataramani·Jonah Casebeer·Paris Smaragdis
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Abstract

Source separation and other audio applications have traditionally relied on the use of short-time Fourier transforms as a front-end frequency domain representation step. The unavailability of a neural network equivalent to forward and inverse transforms hinders the implementation of end-to-end learning systems for these applications. We present an auto-encoder neural network that can act as an equivalent to short-time front-end transforms. We demonstrate the ability of the network to learn optimal, real-valued basis functions directly from the raw waveform of a signal and further show how it can be used as an adaptive front-end for supervised source separation. In terms of separation performance, these transforms significantly outperform their Fourier counterparts. Finally, we also propose a novel source to distortion ratio based cost function for end-to-end source separation.

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Shrikant Venkataramani, Jonah Casebeer, Paris Smaragdis. 2017-05-06. End-to-end Source Separation with Adaptive Front-Ends. https://arxiv.org/abs/1705.02514

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