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.
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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