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Sound Localization by Self-Supervised Time Delay Estimation

2022-04-26 17:59:01
Ziyang Chen, David F. Fouhey, Andrew Owens
arXiv_CV
           
Abstract

Sounds reach one microphone in a stereo pair sooner than the other, resulting in an interaural time delay that conveys their directions. Estimating a sound's time delay requires finding correspondences between the signals recorded by each microphone. We propose to learn these correspondences through self-supervision, drawing on recent techniques from visual tracking. We adapt the contrastive random walk of Jabri et al. to learn a cycle-consistent representation from unlabeled stereo sounds, resulting in a model that performs on par with supervised methods on "in the wild" internet recordings. We also propose a multimodal contrastive learning model that solves a visually-guided localization task: estimating the time delay for a particular person in a multi-speaker mixture, given a visual representation of their face. Project site: this https URL

Abstract (translated)

URL

https://arxiv.org/abs/2204.12489

PDF

https://arxiv.org/pdf/2204.12489.pdf

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