AUDRON: A Deep Learning Framework with Fused Acoustic Signatures for Drone Type Recognition

• URL: http://arxiv.org/abs/2512.20407v2
• Date: Tue, 30 Dec 2025 12:35:03 GMT
• Title: AUDRON: A Deep Learning Framework with Fused Acoustic Signatures for Drone Type Recognition
• Authors: Rajdeep Chatterjee, Sudip Chakrabarty, Trishaani Acharjee, Deepanjali Mishra,
• Abstract summary: Unmanned aerial vehicles (UAVs) are increasingly used across diverse domains, including logistics, agriculture, surveillance, and defense.<n> Acoustic sensing offers a low-cost and non-intrusive alternative to vision or radar-based detection.<n>This study introduces AUDRON, a hybrid deep learning framework for drone sound detection.
• Score: 1.8665975431697428
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unmanned aerial vehicles (UAVs), commonly known as drones, are increasingly used across diverse domains, including logistics, agriculture, surveillance, and defense. While these systems provide numerous benefits, their misuse raises safety and security concerns, making effective detection mechanisms essential. Acoustic sensing offers a low-cost and non-intrusive alternative to vision or radar-based detection, as drone propellers generate distinctive sound patterns. This study introduces AUDRON (AUdio-based Drone Recognition Network), a hybrid deep learning framework for drone sound detection, employing a combination of Mel-Frequency Cepstral Coefficients (MFCC), Short-Time Fourier Transform (STFT) spectrograms processed with convolutional neural networks (CNNs), recurrent layers for temporal modeling, and autoencoder-based representations. Feature-level fusion integrates complementary information before classification. Experimental evaluation demonstrates that AUDRON effectively differentiates drone acoustic signatures from background noise, achieving high accuracy while maintaining generalizability across varying conditions. AUDRON achieves 98.51 percent and 97.11 percent accuracy in binary and multiclass classification. The results highlight the advantage of combining multiple feature representations with deep learning for reliable acoustic drone detection, suggesting the framework's potential for deployment in security and surveillance applications where visual or radar sensing may be limited.

Related papers

• A Novel Unified Lightweight Temporal-Spatial Transformer Approach for Intrusion Detection in Drone Networks [0.24790748620223146]
  Existing intrusion detection mechanisms often lack the adaptability, efficiency, and generalizability required for drone networks.<n>This paper proposes TSLT-Net, a novel lightweight and unified Temporal Spatial Transformer based intrusion detection system tailored specifically for drone networks.
  arXiv  Detail & Related papers  (2025-10-03T04:23:21Z)
• Wavelet-Guided Dual-Frequency Encoding for Remote Sensing Change Detection [67.84730634802204]
  Change detection in remote sensing imagery plays a vital role in various engineering applications, such as natural disaster monitoring, urban expansion tracking, and infrastructure management.<n>Most existing methods still rely on spatial-domain modeling, where the limited diversity of feature representations hinders the detection of subtle change regions.<n>We observe that frequency-domain feature modeling particularly in the wavelet domain amplify fine-grained differences in frequency components, enhancing the perception of edge changes that are challenging to capture in the spatial domain.
  arXiv  Detail & Related papers  (2025-08-07T11:14:16Z)
• Robust Low-Cost Drone Detection and Classification in Low SNR Environments [0.9087641068861043]
  We evaluate various convolutional neural networks (CNNs) for their ability to detect and classify drones. We demonstrate a low-cost drone detection system using a standard computer, software-defined radio (SDR) and antenna.
  arXiv  Detail & Related papers  (2024-06-26T12:50:55Z)
• Multi-Stage Fusion Architecture for Small-Drone Localization and Identification Using Passive RF and EO Imagery: A Case Study [0.1872664641238533]
  This work develops a multi-stage fusion architecture using passive radio frequency (RF) and electro-optic (EO) imagery data. Supervised deep learning based techniques and unsupervised foreground/background separation techniques are explored to cope with challenging environments. The proposed fusion architecture is tested and the tracking and performance is quantified over the range.
  arXiv  Detail & Related papers  (2024-03-30T22:53:28Z)
• Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Learning [81.98675881423131]
  This research addresses the challenge of developing a universal deepfake detector that can effectively identify unseen deepfake images. Existing frequency-based paradigms have relied on frequency-level artifacts introduced during the up-sampling in GAN pipelines to detect forgeries. We introduce a novel frequency-aware approach called FreqNet, centered around frequency domain learning, specifically designed to enhance the generalizability of deepfake detectors.
  arXiv  Detail & Related papers  (2024-03-12T01:28:00Z)
• A Two-Dimensional Deep Network for RF-based Drone Detection and Identification Towards Secure Coverage Extension [7.717171534776764]
  We use Short-Time Fourier Transform to extract two-dimensional features from the raw signals, which contain both time-domain and frequency-domain information. Then, we employ a Convolutional Neural Network (CNN) built with ResNet structure to achieve multi-class classifications. Our experimental results show that the proposed ResNet-STFT can achieve higher accuracy and faster convergence on the extended dataset.
  arXiv  Detail & Related papers  (2023-08-26T15:43:39Z)
• Echoes Beyond Points: Unleashing the Power of Raw Radar Data in Multi-modality Fusion [74.84019379368807]
  We propose a novel method named EchoFusion to skip the existing radar signal processing pipeline. Specifically, we first generate the Bird's Eye View (BEV) queries and then take corresponding spectrum features from radar to fuse with other sensors.
  arXiv  Detail & Related papers  (2023-07-31T09:53:50Z)
• Histogram Layer Time Delay Neural Networks for Passive Sonar Classification [58.720142291102135]
  A novel method combines a time delay neural network and histogram layer to incorporate statistical contexts for improved feature learning and underwater acoustic target classification. The proposed method outperforms the baseline model, demonstrating the utility in incorporating statistical contexts for passive sonar target recognition.
  arXiv  Detail & Related papers  (2023-07-25T19:47:26Z)
• Deep Spectro-temporal Artifacts for Detecting Synthesized Speech [57.42110898920759]
  This paper provides an overall assessment of track 1 (Low-quality Fake Audio Detection) and track 2 (Partially Fake Audio Detection) In this paper, spectro-temporal artifacts were detected using raw temporal signals, spectral features, as well as deep embedding features. We ranked 4th and 5th in track 1 and track 2, respectively.
  arXiv  Detail & Related papers  (2022-10-11T08:31:30Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• Spotting adversarial samples for speaker verification by neural vocoders [102.1486475058963]
  We adopt neural vocoders to spot adversarial samples for automatic speaker verification (ASV) We find that the difference between the ASV scores for the original and re-synthesize audio is a good indicator for discrimination between genuine and adversarial samples. Our codes will be made open-source for future works to do comparison.
  arXiv  Detail & Related papers  (2021-07-01T08:58:16Z)
• Amateur Drones Detection: A machine learning approach utilizing the acoustic signals in the presence of strong interference [10.74088633638169]
  Unmonitored deployment of UAVs called the amateur drones (AmDr) can lead to serious security threats and risk to human life and infrastructure. In this paper, we propose an efficient machine learning approach to detect various acoustic signals i.e., sounds of bird, airplanes, thunderstorm, rain, wind and the UAVs.
  arXiv  Detail & Related papers  (2020-02-28T17:28:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.