Acoustic UAV Detection in Battlefield Scenarios: Handling Noise, Domain Shift, and Weak Labels

Vadym Vilhurin; Volodymyr Sydorskyi; Andrii Shevtsov

cs.SDAug 14, 2026

Acoustic UAV Detection in Battlefield Scenarios: Handling Noise, Domain Shift, and Weak Labels

Authors: Vadym Vilhurin, Volodymyr Sydorskyi, Andrii Shevtsov

Organizations: Institute for Applied System Analysis, Igor Sikorsky Kyiv Polytechnic Institute, Kyiv, Ukraine · Zvook, Lviv, Ukraine · Computer Science Department, Kyiv School of Economics, Kyiv, Ukraine

Abstract

Passive acoustic sensing offers a critical, cost-efficient, and, crucially, passive alternative for detecting small unmanned aerial vehicles. However, the practical deployment of acoustic systems is discouraged by extreme environmental noise and sensor-induced domain shift caused by heterogeneous hardware. This paper addresses these challenges by introducing a robust framework optimized for real-world battlefield conditions. We propose the integration of Per-Channel Energy Normalization (PCEN) and attention-based pooling to enhance feature extraction under low signal-to-noise ratio scenarios. We further propose a domain-aware training strategy that leverages auxiliary classes and multi-microphone data to mitigate cross-domain performance degradation. Evaluated on a unique dataset of combat-zone recordings from the Ukrainian frontlines, our approach significantly outperforms existing baselines, increasing the F1 score from 55.4% to 78.6%. This paper was originally presented at the International Conference on Military Communication and Information Systems (ICMCIS), organized by the Information Systems Technology (IST) Scientific and Technical Committee, IST-224-RSY - the ICMCIS, held in Bath, United Kingdom, 12-13 May 2026.

Figures & tables

Fig. 3: Example of a Mel spectrogram (top) and PCEN representation (bottom) for a UAV audio sample.

Train set	Device	Class	Samples statistics
Train set	Device	Class	Count	Length (hours)
Small	Mic-1	Drone	4758	11.9
		Jet Aircraft	6012	15.0
		Helicopter	2559	6.4
		Propeller Aircraft	1797	4.5
		Noise	50000	125.0

TABLE I: Train classes distribution

Set	Device	Class	Samples statistics
Set	Device	Class	Count	Length (hours)
Val	Mic-1	Drone	1969	4.92
		Jet Aircraft	1502	3.75
		Helicopter	2800	7.0
		Propeller Aircraft	412	1.03
		Noise	10765	26.91

TABLE II: Validation and test classes distribution

Shift Type	Model Frontend	Sil. ( $\downarrow$ )	$k$ -NN (%) ( $\downarrow$ )
Global Noise Sensor Shift (Mic-1 vs. Mic-2)	Baseline (Log-Mel)	0.1346	97.56
	Enhanced (Log-Mel)	0.2084	97.84
	Enhanced (PCEN)	0.1700	96.13
Seasonal Shift (Winter vs. Spring)	Baseline (Log-Mel)	0.0093	74.60
	Enhanced (Log-Mel)	0.0086	72.94
	Enhanced (PCEN)	0.0048	70.85

TABLE III: Analysis of domain invariance with respect to seasonal and sensor variability (lower clustering metrics correspond to higher invariance)

Model setup	Validation set		Test set
Model setup	ROC AUC	F1	ROC AUC	F1
Baseline model setup	99.99%	100%	97.07%	66%
Enhanced model setup based on Section VII	99.67%	97%	69.1%	62%

TABLE IV: Adversarial validation results

Model	Parameters	Small drone metrics on validation			Small drone metrics on test set
Model	(M)	F1, %	Precision, %	Recall, %	F1, %	Precision, %	Recall, %
Zvook [ 9 ] Baseline	37	96.35	99.21	93.66	69.10	81.59	59.92
Baseline on Mic2	28.22	17.00	88.00	0.094	17.40	87.70	0.097
Baseline on Mic2 + Mic1	28.22	91.40	100.00	84.10	49.80	88.80	34.60
+ SWA	28.22	96.20	100.00	92.70	59.70	87.20	45.30
+ Mixup	28.22	83.20	72.60	97.40	75.60	74.40	76.90

TABLE V: Experiment results (best metric in category being semibold and the following one underlined )

Fig. 8: ROC curves for different drone distance subgroups and the final ensemble model.

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EchoHawk: A Reproducible Acoustic Pipeline for Drone Detection, Classification, and Direction-Finding, with a Cautionary Study of Session-Level Data Leakage

David Shulman

Data Leakage Direction-of-Arrival Estimation Sound Event Detection Passive Acoustic Monitoring UAV Detection

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Passive acoustic sensing is an attractive modality for counter-unmanned aerial system (counter-UAS) defence: it is covert, low-cost, and effective against drones with small radar cross-sections or minimal radio emissions. We present EchoHawk, an open and fully reproducible reference pipeline that detects a drone from its rotor harmonics, estimates its blade-passing frequency, and localises it with a microphone array via classical wideband beamforming (delay-and-sum, MVDR, MUSIC) and time-delay processing (GCC-PHAT, SRP-PHAT), followed by temporal tracking. We evaluate the system on a physically transparent synthetic benchmark that pits drones against hard low-frequency harmonic confusers, such as ground vehicles, and on real recorded audio. Our central methodological contribution is a documented case of session-level data leakage in a widely used public dataset: because its recordings are pre-segmented into short clips, naive clip-level splits place adjacent slices of the same continuous recording in both training and test sets, inflating reported performance. Enforcing recording-session-grouped cross-validation reduces, for example, a random-forest baseline's detection probability at a 1% false-alarm rate from 0.796 to 0.745, yielding honest numbers. All code, figures, and a synthetic data generator are released so that every result runs without any download.

Abstract

Passive Acoustic Monitoring

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