# SyncNet: correlating objective for time delay estimation in audio signals

arXiv: https://arxiv.org/abs/2203.14639 · HTML: https://mlsanity.com/paper/2203.14639/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2022.

## Built on by (most cited first, 10 of 10)

- [SpeakerVid-5M: A Large-Scale High-Quality Dataset for Audio-Visual Dyadic Interactive Human Generation](https://mlsanity.com/paper/2507.09862/) (2025)
- [RoCo:Robust Collaborative Perception By Iterative Object Matching and Pose Adjustment](https://mlsanity.com/paper/2408.00257/) (2024)
- [SkyReels-V4: Multi-modal Video-Audio Generation, Inpainting and Editing model](https://mlsanity.com/paper/2602.21818/) (2026)
- [Audio-Visual Intelligence in Large Foundation Models](https://mlsanity.com/paper/2605.04045/) (2026)
- [wav2pos: Sound Source Localization using Masked Autoencoders](https://mlsanity.com/paper/2408.15771/) (2024)
- [GenSync: A Generalized Talking Head Framework for Audio-driven Multi-Subject Lip-Sync using 3D Gaussian Splatting](https://mlsanity.com/paper/2505.01928/) (2025)
- [Beyond Time Shifts: Adapting Omni-LLM as a Reference-Free Evaluator for Generative Audio-Visual Models](https://mlsanity.com/paper/2607.09091/) (2026)
- [Efficient and Microphone-Fault-Tolerant 3D Sound Source Localization](https://mlsanity.com/paper/2505.20961/) (2025)
- [Automatic Detection and Analysis of Singing Mistakes for Music Pedagogy](https://mlsanity.com/paper/2602.06917/) (2026)
- [mAVE: A Watermark for Joint Audio-Visual Generation Models](https://mlsanity.com/paper/2603.07090/) (2026)

## Abstract (arXiv)

This study addresses the task of performing robust and reliable time-delay estimation in signals in noisy and reverberating environments. In contrast to the popular signal processing based methods, this paper proposes to transform the input signals using a deep neural network into another pair of sequences which show high cross correlation at the actual time delay. This is achieved with the help of a novel correlation function based objective function for training the network. The proposed approach is also intrinsically interpretable as it does not lose temporal information. Experimental evaluations are performed for estimating mutual time delays for different types of audio signals such as pulse, speech and musical beats. SyncNet outperforms other classical approaches, such as GCC-PHAT, and some other learning based approaches.