mlsanityData to 7 Oct 2026

Sound Localization by Self-Supervised Time Delay Estimation

arXiv 2204.12489 ↗First posted 26 Apr 2022cs.CV cs.SD eess.AScited by 18

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Abstract (arXiv)

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: https://ificl.github.io/stereocrw/

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 51 of its 89 references that are in the map. First posted on arXiv 26 Apr 2022.

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51 of 89 references.

References · 51

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38 more
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Citing papers per year: 2022: 1; 2023: 8; 2024: 6; 2025: 2; 2026: 1.

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