# TAME: Temporal Audio-based Mamba for Enhanced Drone Trajectory Estimation and Classification

arXiv: https://arxiv.org/abs/2412.13037 · HTML: https://mlsanity.com/paper/2412.13037/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Speech and audio SSMs, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 22 of its 42 references that are in the map.
- First posted on arXiv 17 Dec 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Speech and audio SSMs](https://mlsanity.com/ssm/speech-and-audio-ssms/)

## Builds on (most cited first, 22 of 22)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Is Someone Speaking? Exploring Long-term Temporal Features for Audio-visual Active Speaker Detection](https://mlsanity.com/paper/2107.06592/) (2021)
- [SSAMBA: Self-Supervised Audio Representation Learning with Mamba State Space Model](https://mlsanity.com/paper/2405.11831/) (2024)
- [Audio Mamba: Bidirectional State Space Model for Audio Representation Learning](https://mlsanity.com/paper/2406.03344/) (2024)
- [MMAUD: A Comprehensive Multi-Modal Anti-UAV Dataset for Modern Miniature Drone Threats](https://mlsanity.com/paper/2402.03706/) (2024)
- [MoPA: Multi-Modal Prior Aided Domain Adaptation for 3D Semantic Segmentation](https://mlsanity.com/paper/2309.11839/) (2023)
- [The 2nd Anti-UAV Workshop & Challenge: Methods and Results](https://mlsanity.com/paper/2108.09909/) (2021)
- [The 3rd Anti-UAV Workshop & Challenge: Methods and Results](https://mlsanity.com/paper/2305.07290/) (2023)
- [Multi-Modal UAV Detection, Classification and Tracking Algorithm -- Technical Report for CVPR 2024 UG2 Challenge](https://mlsanity.com/paper/2405.16464/) (2024)
- [Investigation of UAV Detection in Images with Complex Backgrounds and Rainy Artifacts](https://mlsanity.com/paper/2305.16450/) (2023)
- [DIRECT: A Differential Dynamic Programming Based Framework for Trajectory Generation](https://mlsanity.com/paper/2109.04686/) (2021)
- [NEPTUNE: Nonentangling Trajectory Planning for Multiple Tethered Unmanned Vehicles](https://mlsanity.com/paper/2212.01536/) (2022)
- [On Onboard LiDAR-based Flying Object Detection](https://mlsanity.com/paper/2303.05404/) (2023)
- [A Cost-Effective Cooperative Exploration and Inspection Strategy for Heterogeneous Aerial System](https://mlsanity.com/paper/2403.01225/) (2024)
- [AV-PedAware: Self-Supervised Audio-Visual Fusion for Dynamic Pedestrian Awareness](https://mlsanity.com/paper/2411.06789/) (2024)
- [Jacquard V2: Refining Datasets using the Human In the Loop Data Correction Method](https://mlsanity.com/paper/2402.05747/) (2024)
- [SPINS: Structure Priors aided Inertial Navigation System](https://mlsanity.com/paper/2012.14053/) (2020)
- [Path Planning for Multiple Tethered Robots Using Topological Braids](https://mlsanity.com/paper/2305.00271/) (2023)
- [ULOC: Learning to Localize in Complex Large-Scale Environments with Ultra-Wideband Ranges](https://mlsanity.com/paper/2409.11122/) (2024)
- [Distance-based Multiple Non-cooperative Ground Target Encirclement for Complex Environments](https://mlsanity.com/paper/2409.15840/) (2024)

## Built on by (most cited first, 2 of 2)

- [Securing the Skies: A Comprehensive Survey on Anti-UAV Methods, Benchmarking, and Future Directions](https://mlsanity.com/paper/2504.11967/) (2025)
- [Separating Drone Point Clouds From Complex Backgrounds by Cluster Filter -- Technical Report for CVPR 2024 UG2 Challenge](https://mlsanity.com/paper/2412.16947/) (2024)

## Abstract (arXiv)

The increasing prevalence of compact UAVs has introduced significant risks to public safety, while traditional drone detection systems are often bulky and costly. To address these challenges, we present TAME, the Temporal Audio-based Mamba for Enhanced Drone Trajectory Estimation and Classification. This innovative anti-UAV detection model leverages a parallel selective state-space model to simultaneously capture and learn both the temporal and spectral features of audio, effectively analyzing propagation of sound. To further enhance temporal features, we introduce a Temporal Feature Enhancement Module, which integrates spectral features into temporal data using residual cross-attention. This enhanced temporal information is then employed for precise 3D trajectory estimation and classification. Our model sets a new standard of performance on the MMUAD benchmarks, demonstrating superior accuracy and effectiveness. The code and trained models are publicly available on GitHub https://github.com/AmazingDay1/TAME.