Интерпретируемая акустическая классификация малоразмерных летательных аппаратов с использованием метода Grad-CAM

Минаков, Е. И.; Гридин, В. Н.; Андронычев, Д. Г.; Солодовников, В. И.; Сычугов, А. А.; Французова, Ю. В.

doi:10.30898/1684-1719.2025.11.4

Е.И. Минаков¹, В.Н. Гридин², Д.Г. Андронычев¹, В.И. Солодовников², А.А. Сычугов¹, Ю.В. Французова¹

Аннотация

Задача своевременного обнаружения и классификации малоразмерных летательных аппаратов осложняется ограниченной точностью традиционных спектральных методов и их низкой помехоустойчивостью. Цель работы - разработать интерпретируемую модель акустической классификации, которая сочетает в себе высокую точность и прозрачность принятия решений. В работе предложена интерпретируемая модель акустической классификации на основе сверточной нейронной сети, обученной на MFCC-признаках и оптимизированной с помощью Keras Tuner. Для визуализации значимых частотно-временных областей используется метод Grad-CAM. Экспериментальная оценка показала высокое качество распознавания: точность составила 0.97 (95 % ДИ: 0.94-0.99), macro-F1 = 0.98, ROC-AUC = 0.998. Модель продемонстрировала устойчивость к шумовым искажениям до уровня -5 дБ SNR, а карты активации подтвердили, что решения основаны на физических характеристиках сигналов - фундаментальных частотах роторов и их гармониках. Разработанный подход может применяться в системах мониторинга воздушного пространства, защиты объектов и обеспечения безопасности, предоставляя оператору как точный прогноз, так и его интерпретацию.

акустическая классификация
сверточные нейронные сети
признаки MFCC
grad-CAM
визуализация активации
глубокое обучение
спектральный анализ
обработка аудиосигнала
интерпретируемость модели

Abstract (in English)

Interpretable acoustic classification of small-sized aircraft using the Grad-CAM method

The rapid growth of small unmanned aerial vehicles (UAVs) creates new challenges for timely detection and reliable classification, especially in security-sensitive environments. Traditional acoustic analysis methods, usually based on spectral features, often provide limited accuracy and are highly sensitive to environmental noise. Deep learning approaches, particularly convolutional neural networks (CNNs), have demonstrated clear improvements, but their “black-box” nature reduces trust in predictions and complicates deployment in practice. The goal of this study is to develop an interpretable acoustic classification model for small UAVs that combines high recognition accuracy with transparent decision-making. The proposed solution employs a CNN architecture optimized using the Keras Tuner framework, which automatically searches over hyperparameters such as convolutional kernel size, the number of filters, the size of the dense layer, regularization coefficients, and learning rate. The model processes Mel-frequency cepstral coefficients (MFCCs) extracted from UAV recordings, using fixed input dimensions of (40, 173, 1) to represent both spectral and temporal information. The optimized network includes four convolutional blocks with 16, 32, 192, and 128 filters, each followed by batch normalization, ReLU activation, and max pooling. A global average pooling layer reduces feature dimensionality before a dense layer of 64 neurons with dropout regularization, and finally a SoftMax classification layer outputs nine UAV classes. To improve interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) is integrated into the analysis pipeline, enabling visualization of the most relevant time-frequency regions that support classification decisions. Experimental evaluation shows that the model achieves 97 % accuracy (95 % CI: 94-99 %), a macro-F1 score of 0.98, and ROC-AUC of 0.998, while remaining robust to acoustic noise down to 0 dB SNR. Grad-CAM visualizations demonstrate that the network consistently highlights physically meaningful spectral regions - such as fundamental rotor frequencies and their harmonics - while ignoring irrelevant noise. This confirms that the network’s high performance is based on real acoustic patterns characteristic of UAVs rather than spurious artifacts. From a practical perspective, the system can support airspace monitoring, perimeter security, and protection of critical infrastructure by providing not only accurate predictions but also visual explanations that are understandable to human operators. This combination increases user trust and facilitates real-time decision-making in operational environments. The results indicate that incorporating interpretable deep learning methods such as Grad-CAM into UAV acoustic classification systems can significantly enhance both accuracy and transparency. Future work will focus on further improving noise robustness, broadening the range of UAV types considered, and exploring complementary interpretability methods such as Integrated Gradients and SHAP.

Для цитирования

Минаков Е.И., Гридин В.Н., Андронычев Д.Г., Солодовников В.И., Сычугов А.А., Французова Ю.В. Интерпретируемая акустическая классификация малоразмерных летательных аппаратов с использованием метода Grad-CAM // Журнал радиоэлектроники. – 2025. – № 11. https://doi.org/10.30898/1684-1719.2025.11.4

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