Neural Edge Histogram Descriptors for Underwater Acoustic Target Recognition

NDERWATER acoustic target recognition (UATR) is crucial for applications such as environmental monitoring, Deep learning models, such as convolutional neural networks exploration, and ship noise characterization, aiding in (CNNs), excel in feature representation and transfer learning, marine resource management and ocean-based technologies to adapting well to underwater acoustics when pre-trained on enhance ocean monitoring [1], [2]. Passive sonar uses external large vision datasets [11]-[13]. Similarly, pre-trained audio acoustic signals to identify underwater objects without emitting neural networks (PANNs) [14], trained on a large audio dataset sound [2]. Spectrograms, generated through signal processing (AudioSet [15]), have proven effective for passive sonar techniques like Short-Time Fourier Transform (STFT) classification where data scarcity is a challenge [16]. Moreover, and Mel-frequency spectrograms, transform signals into visual transformer-based models, including vision transformers representations, facilitating complex pattern extraction from (ViTs) [17] and audio spectrogram transformers (ASTs) [18], acoustic data [3]-[5].