# SONNET: Enhancing Time Delay Estimation by Leveraging Simulated Audio

arXiv: https://arxiv.org/abs/2411.13179 · HTML: https://mlsanity.com/paper/2411.13179/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 20 Nov 2024.

## Builds on (most cited first, 15 of 15)

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- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [On the Spectral Bias of Neural Networks](https://mlsanity.com/paper/1806.08734/) (2018)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [Pyroomacoustics: A Python package for audio room simulations and array processing algorithms](https://mlsanity.com/paper/1710.04196/) (2017)
- [A Survey of Sound Source Localization with Deep Learning Methods](https://mlsanity.com/paper/2109.03465/) (2021)
- [Robust Sound Source Tracking Using SRP-PHAT and 3D Convolutional Neural Networks](https://mlsanity.com/paper/2006.09006/) (2020)
- [Towards End-to-End Acoustic Localization using Deep Learning: from Audio Signal to Source Position Coordinates](https://mlsanity.com/paper/1807.11094/) (2018)
- [FN-SSL: Full-Band and Narrow-Band Fusion for Sound Source Localization](https://mlsanity.com/paper/2305.19610/) (2023)
- [Extending GCC-PHAT using Shift Equivariant Neural Networks](https://mlsanity.com/paper/2208.04654/) (2022)
- [Soft Label Coding for End-to-end Sound Source Localization With Ad-hoc Microphone Arrays](https://mlsanity.com/paper/2304.07512/) (2023)
- [Graph neural networks for sound source localization on distributed microphone networks](https://mlsanity.com/paper/2306.16081/) (2023)
- [Dual input neural networks for positional sound source localization](https://mlsanity.com/paper/2308.04169/) (2023)
- [An Automatic System for Acoustic Microphone Geometry Calibration based on Minimal Solvers](https://mlsanity.com/paper/1610.02392/) (2016)
- [Using Received Power in Microphone Arrays to Estimate Direction of Arrival](https://mlsanity.com/paper/2305.02915/) (2023)

## Abstract (arXiv)

Time delay estimation or Time-Difference-Of-Arrival estimates is a critical component for multiple localization applications such as multilateration, direction of arrival, and self-calibration. The task is to estimate the time difference between a signal arriving at two different sensors. For the audio sensor modality, most current systems are based on classical methods such as the Generalized Cross-Correlation Phase Transform (GCC-PHAT) method. In this paper we demonstrate that learning based methods can, even based on synthetic data, significantly outperform GCC-PHAT on novel real world data. To overcome the lack of data with ground truth for the task, we train our model on a simulated dataset which is sufficiently large and varied, and that captures the relevant characteristics of the real world problem. We provide our trained model, SONNET (Simulation Optimized Neural Network Estimator of Timeshifts), which is runnable in real-time and works on novel data out of the box for many real data applications, i.e. without re-training. We further demonstrate greatly improved performance on the downstream task of self-calibration when using our model compared to classical methods.