NLP

CORE MACHINE LEARNING

Neural Synthesis of Binaural Speech from Mono Audio

May 4, 2021

Abstract

We present a neural rendering approach for binaural sound synthesis that can produce realistic and spatially accurate binaural sound in realtime. The network takes, as input, a single-channel audio source and synthesizes, as output, two-channel binaural sound, conditioned on the relative position and orientation of the listener with respect to the source. We investigate deficiencies of the l2-loss on raw wave-forms in a theoretical analysis and introduce an improved loss that overcomes these limitations. In an empirical evaluation, we establish that our approach is the first to generate spatially accurate waveform outputs (as measured by real recordings) and outperforms existing approaches by a considerable margin, both quantitatively and in a perceptual study. Dataset and code are available online.

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AUTHORS

Written by

Alexander Richard

Dejan Markovic

Israel D. Gebru

Steven Krenn

Gladstone Butler

Fernando de la Torre

Yaser Sheikh

Publisher

ICLR 2021

Research Topics

Core Machine Learning

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