# LOCUS: LOcalization with Channel Uncertainty and Sporadic Energy

arXiv: https://arxiv.org/abs/2302.09409 · HTML: https://mlsanity.com/paper/2302.09409/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 45 references that are in the map.
- First posted on arXiv 18 Feb 2023.

## Builds on (most cited first, 16 of 16)

- [Learning Deep CNN Denoiser Prior for Image Restoration](https://mlsanity.com/paper/1704.03264/) (2017)
- [A Neural Representation of Sketch Drawings](https://mlsanity.com/paper/1704.03477/) (2017)
- [SMIL: Multimodal Learning with Severely Missing Modality](https://mlsanity.com/paper/2103.05677/) (2021)
- [Are Multimodal Transformers Robust to Missing Modality?](https://mlsanity.com/paper/2204.05454/) (2022)
- [BAM: Bottleneck Attention Module](https://mlsanity.com/paper/1807.06514/) (2018)
- [Pyroomacoustics: A Python package for audio room simulations and array processing algorithms](https://mlsanity.com/paper/1710.04196/) (2017)
- [Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer](https://mlsanity.com/paper/1807.07543/) (2018)
- [Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks](https://mlsanity.com/paper/1807.00129/) (2018)
- [ACCDOA: Activity-Coupled Cartesian Direction of Arrival Representation for Sound Event Localization and Detection](https://mlsanity.com/paper/2010.15306/) (2020)
- [A Dataset of Dynamic Reverberant Sound Scenes with Directional Interferers for Sound Event Localization and Detection](https://mlsanity.com/paper/2106.06999/) (2021)
- [Polyphonic Sound Event Detection and Localization using a Two-Stage Strategy](https://mlsanity.com/paper/1905.00268/) (2019)
- [The LOCATA Challenge: Acoustic Source Localization and Tracking](https://mlsanity.com/paper/1909.01008/) (2019)
- [SALSA: Spatial Cue-Augmented Log-Spectrogram Features for Polyphonic Sound Event Localization and Detection](https://mlsanity.com/paper/2110.00275/) (2021)
- [An Attention-Aided Deep Learning Framework for Massive MIMO Channel Estimation](https://mlsanity.com/paper/2108.09430/) (2021)
- [Multi-Speaker Localization Using Convolutional Neural Network Trained with Noise](https://mlsanity.com/paper/1712.04276/) (2017)
- [Sound Source Localization in a Multipath Environment Using Convolutional Neural Networks](https://mlsanity.com/paper/1710.10948/) (2017)

## Built on by (most cited first, 1 of 1)

- [OWL: Geometry-Aware Spatial Reasoning for Audio Large Language Models](https://mlsanity.com/paper/2509.26140/) (2025)

## Abstract (arXiv)

Accurate sound source localization (SSL), such as direction-of-arrival (DoA) estimation, relies on consistent multichannel data. However, batteryless systems often suffer from missing data due to the stochastic nature of energy harvesting, degrading localization performance. We propose LOCUS, a deep learning framework that recovers corrupted features in such settings. LOCUS integrates three modules: (1) Information-Weighted Focus (InFo) to identify corrupted regions, (2) Latent Feature Synthesizer (LaFS) to reconstruct missing features, and (3) Guided Replacement (GRep) to restore data without altering valid inputs. LOCUS significantly improves DoA accuracy under missing-channel conditions, achieving up to 36.91% error reduction on DCASE and LargeSet, and 25.87-59.46% gains in real-world deployments. We release a 50-hour multichannel dataset to support future research on localization under energy constraints. Our code and data are available at: https://bashlab.github.io/locus_project/